EPA-600/3-76-066
September 1976
Ecological Research Series
SIMULATION OF PESTICIDE MOVEMENT ON
SMALL AGRICULTURAL WATERSHEDS
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Athens, Georgia 30601
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development and application of
environmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ECOLOGICAL RESEARCH series. This series
describes research on the effects of pollution on humans, plant and animal
species, and materials. Problems are assessed for their long- and short-term
influences. Investigations include formation, transport, and pathway studies to
determine the fate of pollutants and their effects. This work provides the technical
basis for setting standards to minimize undesirable changes in living organisms
in the aquatic, terrestrial, and atmospheric environments.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/3-76-066
September 1976
SIMULATION OF PESTICIDE MOVEMENT ON SMALL
AGRICULTURAL WATERSHEDS
by
Ronald T. Adams
and
Frances M. Kuirisu
ESL Incorporated
Sunnyvale, California 94086
Contract Nos. 68-01-0721
68-01-2977
Project Officer
George W. Bailey
Environmental Research Laboratory
Athens, Georgia 30601
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30601
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DISCLAIMER
This report has been reviewed by the Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved
for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the U.S. Environ-
mental Protection Agency, nor does mention of trade names or
commercial products constitute endorsement or recommendation for
use.
11
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ABSTRACT
Simulation of Contaminant Reactions and Movement (SCRAM) is
a computer simulation designed to predict the movement of pesti-
cides from agricultural lands. SCRAM is composed of determinis-
tic submodels which describe the following physical processes:
infiltration, percolation, evaporation, runoff, sediment lossr
pesticide adsorption and desorption in the soil profile, pesti-
cide microbial degradation in the soil profile, and pesticide
volatilization.
SCRAM predictions of these physical processes are compared
to experimental data furnished by the Southeast Environmental
Research Laboratory*in cooperation with the Southern Piedmont
Conservation Research Center. Simulated runoff for two small
watersheds (less than 3 hectares) near Athens, Georgia, agrees
reasonably well with experimental data. Sediment loss is not as
accurately predicted. Predictions of pesticide loss in the run-
off and on the sediment are in reasonable agreement with experi-
mental data if allowance is made for the effects of inaccurately
predicting sediment loss.
Simulated pesticide movement in the soil profile differs
from experimental measurements at the surface and below 10 cm.
Simulated degradation rates are below measured rates early in the
season but are in closer agreement by the end of the season.
Volatilization losses for a single pesticide agree qualitatively
with measured values. The evapotranspiration model was not
evaluated directly.
Further testing and development is recommended to improve the
sediment, degradation, and adsorption-desorption models. With
additional development SCRAM should prove to be a valuable re-
search tool to increase our understanding of how pesticides and
other agricultural pollutants are transported to the aquatic
environment.
This report was submitted in fulfillment of Contract No.
68-01-2977 by ESL Incorporated under the sponsorship of the
Environmental Protection Agency. Work was completed in January
1975.
*Now Environmental Research Laboratory
in
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CONTENTS
Page
Abstract iii
List of Figures vi
List of Tables xvi
Acknowledgements xvii
Sections
I Conclusions 1
II Recommendations 4
III Introduction 7
IV Experimental Program Conducted by EPA/USDA 14
V Simulation Structure 25
VI Simulation Testing 44
VII Mathematical Models and Sensitivity Analysis 110
VIII References 216
IX Appendix A - Users Guide to Scram 225
Appendix B - Scram Program Listing 232
Appendix C - Scram Sample Output 302
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FIGURES
No. Page
1 Location of experimental watersheds 15
2 Schematic of the P-01 watershed (2.70 hectares) 16
3 Schematic of the P-02 watershed (1.29 hectares) 17
4 Schematic of the P-03 watershed (1.20 hectares) 18
5 Schematic of the P-04 watershed (1.38 hectares) 18
6 Flowchart of the master scheduler (simplified version) 28
7 The water cycle 34
8 Scram pesticide cycle 38
9 P-01 watershed: hydrograph for the June 13, 1973,
storm 48
10 P-02 watershed: hydrograph for the June 21, 1973,
storm 48
11 P-01 watershed: hydrograph for the July 8, 1973,
storm 50
12 P-01 watershed: hydrograph for the July 30, 1973,
storm 50
13 P-01 watershed: hydrograph for the September 9,
1973, storm 52
14 P-01 watershed: hydrograph for the September 13,
1973, storm 52
15 P-01 watershed: hydrograph for the December 5,
1973, storm 53
16 P-01 watershed: hydrograph for the December 13,
1973, storm 53
17 P-04 watershed: hydrograph for the May 23,
1973, storm 56
18 P-04 watershed: hydrograph for the May 28,
1973, storm 56
vi
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FIGURES (Continued)
No. Page
19 P-04 watershed: hydrograph for the May 28,
1973, storm (PM) 57
20 P-04 watershed: hydrograph for the June 6,
1973, storm 57
21 P-04 watershed: hydrograph for the August 7,
1973, storm 59
22 P-04 watershed: hydrograph for the September 9,
1973, storm 59
23 P-04 watershed: hydrograph for the September 14,
1973, storm 60
24 P-04 watershed: hydrograph for the December 5,
1973, storm 60
25 P-04 watershed: hydrograph for the December 31,
1973, storm 61
26 P-01 watershed: sediment loss for the June 13,
1973, storm 66
27 P-01 watershed: sediment loss for the June 21,
1973, storm 66
28 P-01 watershed: sediment loss for the July 8,
1973, storm 67
29 P-01 watershed: sediment loss for the July 30,
1973, storm 67
30 P-01 watershed: sediment loss for the September
9, 1973, storm 68
31 P-01 watershed: sediment loss for the September
13, 1973, storm 68
32 P-01 watershed: sediment loss for the December
5, 1973, storm 69
33 P-01 watershed: sediment loss for the December
31, 1973, storm 69
VII
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FIGURES (Continued)
No.
34 P-04 watershed: sediment loss for the May 23,
1973, storm
35 P-04 watershed: sediment loss for the May 28,
1973, storm
36 P-04 watershed: sediment loss for the May 28,
1973, storm
37 P-04 watershed: sediment loss for the June
6, 1973, storm
38 P-04 watershed: sediment loss for the July
8, 1973, storm
39 P-04 watershed: sediment loss for the September
9, 1973, storm
40 P-01 watershed: rate of diphenamid loss in
runoff for the June 13, 1973, storm
41 P-01 watershed: rate of diphenamid loss in
runoff for the June 21, 1973, storm
42 P-01 watershed: rate of diphenamid loss in
runoff for the July 8, 1973, storm
43 P-04 watershed: rate of atrazine loss in
runoff for the May 28, 1973, storm (AM)
44 P-04 watershed: rate of atrazine loss in
runoff for the May 28, 1973, storm (PM)
45 P-04 watershed: rate of atrazine loss in
runoff for the June 6, 1973, storm
46 P-01 watershed: diphenamid loss on the
sediment (yg/g). for the June 13, 1973, storm
47 P-01 watershed: diphenamid loss on the
sediment (yg/g) for the June 21, 1973, storm
Page
72
72
73
73
74
74
77
77
78
78
79
79
80
82
Vlll
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FIGURES (Continued)
No. Page
48 Diagram of core samples used in analysis of
experimental data 86
49 P-01 watershed: simulated and measured distribution
in the soil profile on June 13, 1973 86
50 P-01 watershed: simulated and measured distribution
in the soil profile on July 8, 1973 88
51 P-01 watershed: simulated and measured distribution
in the soil profile on August 1, 1973 90
52 P-01 watershed: simulated and measured distribution
in the soil profile on May 23, 1973 90
53 P-04 watershed: atrazine soil profile distribution
on June 8, 1973 91
54 P-04 watershed: atrazine soil profile distribution
on July 10, 1973 91
55 P-01 watershed: degradation of diphenamid in the
soil profile after application on June 13, 1973 94
56 P-04 watershed: degradation of atrazine in the soil
profile after application on May 11, 1973 94
57 Distribution of trifluralin in the soil profile 97
58 P-01 watershed: trifluralin remaining after
application date 99
59 P-03 watershed: trifluralin remaining after
application data 99
60 P-01 watershed: average trifluralin concentration
as a function of soil depth - 1973 101
61 P-01 watershed: average trifluralin concentration
as a function of soil depth - 1973 101
IX
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FIGURES ( Continued)
No. Page
62 P-01 watershed: simulated volatilization and
diffusion of trifluralin from June to
September, 1973 (D = 10. x 10~6 cm2/sec) 102
63 P-01 watershed: simulation volatilization and
movement of trifluralin from June to
September, 1973 (D = 2 x 10~6 cm2/sec) 102
64 Representative soil column for water movement
and storage 116
65 Moisture potential for selected soil types 119
66 Diffusivity for selected soil types 120
67 P-01 watershed: WATER model sensitivity to soil
type for May 28, 1973, storm 121
68 P-01 watershed: WATER model sensitivity to soil
type for September 9, 1973, storm 121
69 P-01 watershed: WATER model sensitivity to soil
type for December 31, 1973, storm 122
70 WATER model sensitivity to soil layer thickness
(G) for Clay soil (May 28, 1973, storm) 124
71 WATER model sensitivity to soil layer thickness
(G) for Clay soil (September 9,- 1973, storm) 124
72 WATER model sensitivity to soil layer thickness
(G) for Clay soil (December 31, 1973, storm) 125
73 WATER model sensitivity to soil layer thickness
(G) for Geary soil (May 28, 1973, storm) 125
74 WATER model sensitivity to soil layer thickness
(G) for Geary soil (September 9, 1973, storm) 126
75 WATER model sensitivity to soil layer thickness
(G) for Geary soil (December 31, 1973, storm) 126
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FIGURES (Continued)
No. Page
76 WATER model sensitivity to initial soil moisture
for Clay soil (May 28, 1973, storm) 129
77 WATER model sensitivity to initial soil moisture
content for Clay soil (September 9, 1973, storm) 129
78 WATER model sensitivity to initial soil moisture
for Clay soil (December 31, 1973, storm) 130
79 WATER model sensitivity to initial soil moisture
for Geary soil (May 28, 1973, storm) 130
80. WATER model sensitivity to initial soil moisture
for Geary soil (September 9, 1973, storm) 131
81 WATER model sensitivity to initial soil moisture
for Geary soil (December 31, 1973, storm) 131
82 Schematic of upland area used to develop
Foster-Meyer sediment model 139
83 Sensitivity of sediment load to slope 153
84 Sensitivity of sediment load to rainfall intensity 153
85 Sensitivity of sediment load to length of the
slope 155
86 Sensitivity of sediment load to the number of
subdivisions down the slope 155
87 Sensitivity of sediment load to the constant,
K,. = ST associated with rainfall detachment 158
88 Sensitivity of sediment load to the constant, K-
associated with rill flow detachment capability 158
89 Sensitivity of sediment load to the constant, K,
associated with transport capacity 160
90 Sensitivity of sediment load to runoff depth 160
XI
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FIGURES (Continued)
No. gage
91 Layer thickness vs solution concentration
distribution 166
92 Layer thickness vs adsorbed concentration
distribution 166
93 Adsorption exponent vs solution concentration
distribution 167
94 Adsorption exponent vs adsorbed concentration
distribution 167
95 Desorption exponent vs solution concentration
distribution 169
96 Desorption exponent vs adsorbed concentration
distribution 169
97 Diffusion coefficient vs solution concentration
distribution 170
98 Diffusion coefficient vs adsorbed concentration
distribution 170
99 P-01 watershed: percent of applied diphenamid
remaining during the 1973 growing season based
on averaged core sample data 174
100 P-02 watershed: percent of applied atrazine
remaining during the 1973 growing season based
on averaged core sample data 174
101 P-03 watershed: percent of applied diphenamid
remaining during the 1973 growing season based
on averaged core sample data 175
102 P-04 watershed: percent of applied atrazine
remaining during the 1973 growing season based
on averaged core sample data 175
103 P-01 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data 176
XII
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FIGURES (Continued)
No. Page
104 P-02 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data 176
105 P-03 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data 177
106 P-04 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data 177
107 Percent of applied diphenamid remaining on
attenuation plots during the 1972 growing season
averaged over all samples 178
108 Percent of applied paraquat remaining on
attenuation plots during the 1972 growing season
averaged over all samples 178
109 Watershed P-01: comparison of simulated versus
actual diphenamid degradation 179
110 Watershed P-04: comparison of simulated versus
actual atrazine degradation 179
111 Sensitivity of the degradation model to moisture
at 0°C 181
112 Sensitivity of the degradation model to moisture
at 10°C 181
113 Sensitivity of the degradation model to
moisture at 20°C 182
114 Sensitivity of the degradation model to
moisture at 30°C 182
115 Sensitivity of the degradation model to
moisture at 30°C 183
116 Sensitivity of the degradation model to
moisture at 20°C 183
xiii
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FIGURES (Continued)
NO.
117 Sensitivity of the degradation model to
temperature at minimal moisture (0%) 185
118 Sensitivity of the degradation model to
temperature at optimal moisture (17.5%) 185
119 Sensitivity of the degradation model to
temperature at maximum moisture (35%) 186
120 Sensitivity of the degradation model to the
pesticide specific parameter-AK 187
121 Sensitivity of the degradation model to the
pesticide specific parameter-BK 187
122 Measured trifluralin distribution in the soil
profile after application, 1973 192
123 Calculated pesticide flux for different initial
conditions 194
124 Pesticide remaining for different initial
conditions 194
125 Calculated trifluralin diffusion coefficient for
Mexico Silt Loam (Bulk density 1.4 g/cc) 196
126 Calculated trifluralin diffusion coefficient for
Mexico Silt Loam (Bulk density 1.0 g/cc) 196
127 Sensitivity of Model II (Mod 1) to the diffusion
coefficient (D) 199
128 Sensitivity of Model II (Mod 1) to pesticide
distribution in the soil profile
(D = 8.64 x 10-2 cm-2/day) 199
129 Trifluralin soil profile concentration predicted
by Model II (Mod 2) for D = 8.64 x 10~3 cm2/day 201
130 Trifluralin soil profile concentration predicted
by Model II (Mod 2) for D = 8.64 x 10~2 cm2/day 202
xiv
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FIGURES (Continued)
No. Page
131 Potential evapotranspiration model sensitivity
to net solar radiation 211
132 Potential evapotranspiration model sensitivity
to relative humidity 211
133 Potential evapotranspiration model sensitivity
to stomata/surface resistance T 212
s
133 Potential evapotranspiration model sensitivity
to roughness parameter z, between 0 and 1 cm 212
135 Potential evapotranspiration model sensitivity
to roughness parameter z-, 214
136 Potential evapotranspiration model sensitivity
to wind speed 214
137 Potential evapotranspiration model sensitivity
to air temperature 215
138 Potential evapotranspiration model sensitivity
to height (Z~) of meteorological measurements 215
xv
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NO.
Paqe
1 EPA/USDA field experimental test site data for
1973 20
2 Properties of herbicides applied on EPA/USDA
test sites 21
3 Environmental parameters recorded with the
PDP8/E data acquisition system on six of the
attenuation plots 23
4 ADDE parameters used in the scram simulation of
pesticide movement on watersheds P-01 and P-04 85
5 Procedure for calculating the percent pesticide
per sample level 85
6 P-01 watershed: measured vs simulated runoff,
sediment and diphenamid loss - June to
December, 1973 103
7 P-04 watershed: measured vs simulated runoff,
sediment, and atrazine loss - May to
December, 1973 106
8 Rainfall characteristics for three storms in 1973 117
9 Runoff volume (liters) by soil type 122
10 Runoff volume (liters) as a function of soil layer
thickness for clay soil 123
11 Runoff volume (liters) as a function of soil layer
thickness for geary soil 127
12 Experimental values for K., 149
13 Predicted values of sediment load from Foster
and Meyer 150
14 Percent pesticide remaining after 100 days as a
function of initial distribution and diffusion
coefficient 198
xvi
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ACKNOWLEDGMENTS
Satisfactory completion of this project involved the efforts
of many people. ESL personnel designed the simulation (SCRAM),
implemented mathematical submodels describing pesticide movement
from small agricultural sources, tested SCRAM against field
experimental data, and contributed to the final report. Specific
responsibilities were as follows:
Mr. R.T. Adams
Mr. M.S. Bull
Dr. R.S. DeZur
Mr. R.G. Donald
Ms. M.K. Jauregui
Mrs. L.T. Kember
Ms. F.M. Kurisu
Project management and implementation
of the runoff and volatilization models
Programming and computer plotting.
Implementation of the sediment model.
Simulation structure and programming.
Implementation of the degradation model,
Programming.
Deputy project management and imple-
mentation of adsorption/desorption
model.
Miss. M.L. Wilson Editorial and publication support.
The members of the U.S. Environmental Protection Agency,
Southeast Environmental Research Laboratory (EPA/SERL) staff,
provided direction, encouragement, and assistance with the tre-
mendous volume of experimental data used to test the simulation.
Special acknowledgment is due Dr. G.W. Bailey, the Project
Officer, and the following SERL personnel:
xvn
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Dr. D.S. Brown
Mr. D.M. Cline
Dr. S.W. Karickhoff
Dr. H.P. Nicholson
Mr. C.N. Smith
Dr. W.C. Steen.
The field experimental data collection program was cospon-
sored by the Southern Piedmont Conservation Research Center
(SPCRC), Agricultural Research Service (ARS), United States De-
partment of Agriculture (USDA). Dr. Ralph A. Leonard and other
SPCRC staff members made significant contributions.
Dr. J.M. Davidson of Oklahoma State University provided
assistance and support with the implementation of his pesticide
adsorption/desorption model. Dr. W.J. Farmer of the University
of California, Riverside, provided technical assistance in the
implementation of his pesticide volatilization model.
xvm
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SECTION I
CONCLUSIONS
1. Simulation of Contaminant Reactions and Movement (SCRAM),
a computer simulation based upon deterministic submodels, is a
valuable tool in understanding how pesticides are transported
from agricultural lands to the aquatic environment.
2. The use of deterministic submodels (rather than statis-
tical submodels) significantly increases the amount of computer
storage and processing time required to simulate a typical grow-
ing season. SCRAM requires 372,000 words of storage on an IBM
370/145 and takes approximately two hours of CPU time to simulate
a 3-4 month growing season. However, the advantages of being
able to predict the pesticide distribution in the soil profile
and soil moisture profile are important in understanding how
pesticides are transported to the aquatic environment.
3. Simulation of surface runoff from small watersheds near
Athens, Georgia, agrees reasonably well with experimental mea-
surements. Additional refinement of the hydrologic submodel will
improve the results for the winter storms.
4. Sediment loss predictions do not agree with experiment-
al measurements. The reasons for the disagreement may reflect
(1) inadequacies of the modified Foster-Meyer submodel, and/or
(2) the physical design of the experimental watersheds, which
alters the natural flow of runoff.
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5. Simulated diphenamid loss in the runoff water and on
the sediment for a small watershed of 2.70 hectares agrees with
experimental measurements. Atrazine loss from a 1.4 hectare
plot was not accurately predicted, primarily because of low
runoff and sediment loss predictions.
6. Pesticide movement in the soil profile depends on the
amount of water infiltrated, and percolated, and on the rate
of evaporation and transpiration. Accordingly, differences
between simulated and experimental pesticide distributions in
the soil profile depend on many processes other than the pesti-
cide adsorption/desorption submodel. Nevertheless, some general
observations are possible:
(a) Some diphenamid is transported below five
centimeters more rapidly than predicted.
(b) Some diphenamid remains in the upper five
centimeters longer than the predicted time.
(c) Initial movement of atrazine into the soil
profile is more rapid than predicted.
(d) Regardless of the pesticide type, the
simulated rate of removal from the soil
surface is too rapid.
7. Simulated degradation of diphenamid is in qualitative
agreement with experimental results. Simulated atrazine de-
gradation did not agree with experimental measurements. Adjust-
ments to the degradation submodel, plus the addition of a soil
temperature submodel, would improve the results.
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8. Simulated volatilization losses of trifluralin are some-
what unsatisfactory. Total simulated losses agree with experi-
mental losses only for unexpectedly large values for the diffu-
sion coefficient. Trifluralin movement in the soil profile is in
close agreement with experimental results.
9. Further development and testing of SCRAM is required
before it can be used effectively to predict the water quality
impact resulting from applications of pesticides to agricultur-
al lands.
10. Simulation can be a valuable technique for developing
effective controls to reduce pesticide pollution of the aquatic
environment. Parameters determined from laboratory tests on
pesticides can be used to simulate the environmental impact.
Quantitative comparisons between pesticides can be developed
for the same simulated conditions. Pesticides which have a
high potential for transport may be restricted to uses where
there is little threat to the aquatic environment.
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SECTION II
RECOMMENDATIONS
In the future, nonpoint sources of water pollution will
be an increasingly significant factor in our nation's ability to
meet the water quality standards specified in the 1972 Federal
Water Pollution Control Act. Simulation is potentially a valua-
ble technique for quantifying the degradation of water quality
by nonpoint sources and for developing effective controls to re-
duce nonpoint source pollution.
Simulation of pesticide movement from agricultural lands
using deterministic (as opposed to statistical) models appears
feasible based upon the results of this project. Development
and testing of a large computer simulation program like SCRAM
leads naturally to the following recommendations:
1. Perform additional testing of the entire simulation
using existing experimental data. The results would
provide the necessary information to make changes and
improvements to SCRAM.
2. The hydrologic model should be modified to include
interflow and groundwater flow. Changes should also be
made to account for different hydrologic properties as
a function of soil depth.
3. Modifications should be made to the evapotranspira-
tion model to improve the algorithm which extracts and
redistributes water in the soil profile.
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This algorithm affects the initial soil moisture
profiles for subsequent storms, thereby altering runoff
volumes, runoff rates, and sediment loads. Additionally,
it indirectly influences all the pesticide predictions by
altering the moisture profile used to degrade the pesti-
cide and by affecting infiltration velocities that deter-
mine adsorption-desorption profiles, thus altering the
pesticide in the runoff water and sediment.
4. A soil temperature predictive model should be
developed and incorporated into SCRAM to predict a soil
temperature profile as a function of such external
variables as crop canopy and meteorological conditions.
It is impractical to use experimental data, which will
generally not be available. Soil temperature profile
is an input to the degradation model.
5. The sediment model should be examined in detail
to determine why the simulated results do not agree
with the experimental results. This model is critical
to the overall success of the simulation. More testing
should be done and the impact, if any, of the present
experimental procedure on sediment loss should be
determined.
6. The pesticide adsorption-desorption model should
be modified to incorporate a pesticide application
algorithm. The pesticide cannot be assumed to dissolve
at the surface during the first rainfall. Also, the
present model requires soil depth increments of less
than 0.5 centimeters, which is incompatible with other
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submodels. Finally, this model should be modified to
permit pesticide degradation and allow for pesticide
in a crystalline state.
7. SCRAM should be tested on watersheds larger than
three hectares. As part of this effort additional
models and algorithms should be developed to define
the interrelationships between each zone on the water-
shed and to permit different crop types and conservation
management practices on each zone.
8. Finally, the applicability of SCRAM to other types
of agricultural pollutants and other nonpoint sources
of pollution should be investigated and implemented if
appropriate.
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SECTION III
INTRODUCTION
BACKGROUND DATA
Pesticides - with their capacity to kill insects, weeds,
rodents, and fungus - combine with machinery, fertilizers, and
new seed types to make American farmers the most productive on
earth. Economic savings due to increased crop production have
been estimated at more than 4.5 billion dollars per year. The
use of chemical pesticides has also stirred intense controversy
and concern over the real and presumed hazards they create in
the environment.
Pesticides differ widely in chemical and toxicological
characteristics. Presently there are thousands of registered
formulations incorporating nearly 900 different chemicals. U.S.
production of pesticides totaled 0.5 billion kilograms in 1971.
Trends in production indicate an annual increase of 15 percent,
2
plus predictions of increasing demand during the next decade.
The pesticides of greatest concern are those that are per-
sistent for long periods and therefore accumulate in the envi-
ronment. Chlorinated hydrocarbon insecticides are a notable
example. Regardless of how they enter organisms, chlorinated
hydrocarbons have an adverse effect on the nervous system.
Mild concentrations cause headaches, dizziness, gastrointestinal
disturbances, numbness and weakness of the extremities, hyperir-
ritability, and apprehension. Higher concentrations are asso-
ciated with muscular fasciculations spreading throughout the
4
body, followed in some cases by convulsions and death.
-------
Due to the absence of human volunteers, most safe human
exposure levels are derived from studies with mice. In one
study using tumorsusceptible mice, increased incidences of tumors
were produced with large doses of DDT (46.4 mg/kg/day). Another
study with mice over five generations showed a greater incidence
of malignancies and leukemia after the second generation.
Other studies involving a variety of chlorinated hydrocarbons have
demonstrated that some compounds are highly toxic while others
produced no effects in mammals (rats and dogs).
Organophosphorus (e.g., parthion) and carbonate insecticides
ingested over prolonged periods result in the dysfunction of
cholinesterase (destruction of acetylcholine, which prevents
Q
reexcitation of muscle fiber) of the nervous system. Studies
involving the toxicity of the chlorophenoxy herbicides (2,4-D;
2,4,5-T; etc.) are inconclusive, but apparently adverse effects
2
are associated with very high doses.
Documented ill effects of pesticides are not limited to
humans but include birds, shellfish, wildlife, and beneficial in-
sects. Between 1966 and 1968 more than 30 percent of the bald
eagles found dead in the United States had lethal levels of
9
dieldrin in the brain. Many of the 48 bald eagles found dead
in Wyoming in 1971 had been killed by thallium, a toxic poison
9
used in animal control. Coho salmon, lake trout, chubs, and
lake herring from Lake Michigan are not considered acceptable
4
for sale in interstate commerce because of high levels of DDT.
An added complication exists in aquatic organisms which
accumulate ingested pesticides. The transfer of pesticide resi-
dues from prey to predator ultimately results in residues in
the higher trophic levels many thousand times greater than am-
bient water levels (biomagnification). The result may be lethal
9
to large predatory birds and mammals.
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Thus, while pesticides significantly contribute to agri-
cultural productivity, it has become apparent that the danger to
man and the environment may outweigh the benefits. Increased
knowledge of the effects of pesticides on ecosystems has resulted
in pressure for new legislation governing the use of pesticides.
LEGISLATIVE BACKGROUND
Federal responsibility for the control of pesticides was
transferred primarily to the United States Environmental Protec-
tion Agency (EPA) when it was established in December, 1970.
Several major Federal laws are available to the EPA for control-
ling pesticides. In 1972 Congress passed the Federal Environ-
mental Pesticide Control Act (FEPCA) which amended the Federal
Insecticide, Fungicide, and Rodenticide Act (FIFRA) of 1947.
Portions of the Federal Water Pollution Control Act (FWPCA)
(as amended in 1972) and of the Federal Food, Drug, and Cosmetic
12
Act are applicable to pesticide control.
FEPCA continues FIFRA's use of product registration as a
basis for control. A full sample label and product formula must
be submitted. The label must contain a description of the pro-
duct's capability and clear directions for its use. Manufac-
turers must show that the product can perform its intended func-
tions without causing unreasonable adverse effects on the envi-
ronment.
A pesticide may be registered for general or restricted use
depending on the product's possible unreasonable adverse effects
on the environment. A product is registered for general use if
it is unlikely to have adverse effects if properly used. Pesti-
cides which may produce adverse effects are registered for re-
stricted use and may only be used under the direction of a certi-
fied applicator. Under this classification of pesticides, denial
of registration would only be possible if a pesticide would cause
-------
unreasonable adverse effects on the environment regardless of
regulatory restrictions. However, FEPCA also provides for a
change in classification or cancellation after initial registra-
tion if evidence subsequently develops that the pesticide gener-
ally causes unreasonably adverse effects on the environment.
FEPCA also extends regulation to the manufacturer's premis-
es, which must be registered with the EPA. This requirement
provides information on the production and distribution of pesti-
cides. Inspection of registered premises may occur upon written
notice to the owner, whether or not a violation of the Act's
provisions is suspected.
Under the Food, Drug and Cosmetic Act, pesticides which are
used in a manner which leaves a residue on crops that provide
food for man or animal are subject to tolerance specifications.
Manufacturers are required to submit information to support the
amount of pesticide residue (tolerance) which can safely remain
on the crop after harvest. Where the supporting data is inade-
quate or a health hazard exists, zero tolerances may be specifi-
ed.
The amendments to the Federal Water Pollution Control Act
of 1972 contain several provisions directed toward nonpoint
source pollution control. Nonpoint sources are not defined in
the Act but are cited in several Sections and include agriculture,
silviculture, mining, and construction activities. Pesticides
are a predominant pollutant from nonirrigated farming and hence
the nonpoint source provisions of FWPCA are available to the
EPA to control pesticide pollution.
10
-------
EPA's efforts to control nonpoint sources involves two
approaches. The first is the identification and application of
the best practical control technologies through Federal, State,
and local mechanisms. The second element is a broad based effort
to assess and control the water quality impact of nonpoint
sources. These efforts should help to implement farm management
practices at the local level, such as terracing, diversions, con-
touring, stripcropping, crop rotations, and cover crops which
reduce water erosion on farm lands.
In order to fully implement FWPCA 1972, the EPA will need to
develop and verify procedures for (1) estimating pesticide dis-
charges from agricultural sources, and (2) predicting reductions
in pesticide discharges resulting from implementation of specific
controls. A first step in this process will require an under-
standing of how pesticides are transported from agricultural
lands to the aquatic environment.
MOVEMENT OF AGRICULTURAL PESTICIDES TO THE AQUATIC ENVIRONMENT
The pathways pesticides follow from the time of application
to argricultural lands until they reach the aquatic environment
have been delineated in detail elsewhere. ' Briefly, there are
two major pathways: dissolution in runoff water, and adsorption
on sediment carried by runoff water. Depending on the pesticide,
rate and mode of application, and soil type, one or both mecha-
14
nisms may be present. Some pesticides are highly volatile and
are not readily transported in runoff water or on sediment.
Nevertheless, they may be deposited in the water systems. Other
pesticides which are persistent may be leached from the soil as
rainwater percolates through the soil. Eventually these pesti-
cides may reach groundwater and be transported into the rivers
and lakes. Finally, pesticides may be directly applied to water-
bodies via poor application techniques.
11
-------
Unfortunately, although the potential pathways for pesticide
movement are relatively easy to identify, their relationship and
significance to each pesticide is not easily quantified. Rainfall
occurs without producing runoff or heavy rainfall and runoff/ may
occur shortly after application. Some pesticides are surface
applied and readily interact with runoff water; others are incor-
porated into the soil. Adsorption of some pesticides in the soil
is so strong that very little pesticide appears in the runoff
water. Tillage systems and conservation practices including
terraces, diversions, stripcropping, and contouring have a signi-
ficant impact on the amount of runoff and soil erosion. Pesti-
cides on the surface and in the soil undergo microbial, chemical,
and photochemical degradation. These processes in turn are in-
fluenced by solar radiation, relative humidity, and soil moisture.
Volatilization depends on the pesticide type, soil moisture, soil
temperature, and wind velocities.
Understanding these phenomenon and developing effect tech-
niques for controlling pesticide contamination of the environment
can be accomplished with the aid of systems analysis and mathe-
matical modelling.
SYSTEMS ANALYSIS AND MATHEMATICAL MODELLING OF PESTICIDE TRANSPORT
The systems analysis approach to problem solving involves a
number of more or less standard steps:
1. Formulation of the problem.
2. Construction of mathematical models that describe
the significant variables of the system.
12
-------
3. Development of a simulation structure compatible
with selected mathematical models.
4. Collection of data to allow estimation of the
model parameters.
5. Testing of the model, proposed solutions and
sensitivity analysis of the parameters, i.e.,
simulation of the system.
6. Identification of the best solutions.
The first step formulation of the pesticide problem has been
reviewed in this section and is covered in detail in the refer-
1413
ences. ' ' Construction of mathematical models to describe
runoff, sedimentation, and pesticide movement is discussed in
this report. The simulation structure developed to accommodate
the mathematical models is discussed in Section V. Data collec-
tion was performed independently but is presented in Section IV.
The initial testing of the simulation and models and the sensi-
tivity analysis comprise Section VII. The final step, identifi-
cation of the best control methodologies to reduce pesticide
contamination of the aquatic environment, will require additional
model development and simulation in the future.
13
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SECTION IV
EXPERIMENTAL PROGRAM CONDUCTED BY EPA/USDA
GENERAL
SCRAM was developed as part of a large program conducted
by the U.S. Environmental Protection Agency's Southeast Environ-
mental Research Laboratory (SERL). Data to support the model
development came from an extensive field investigation effort
conducted by SERL in cooperation with Southern Piedmont Conser-
vation Research Center of the Agricultural Research Service
(ARS), U.S. Department of Agriculture (USDA). This Section
summarizes the joint EPA/USDA field program to facilitate the
understanding of the entire project.
EPA/USDA FIELD SITES
The field program was started in 1972 with the establishment
of two watersheds, two small scale plots, and twelve attenuation
plots. The program was expanded with two additional watersheds
during 1973. The watersheds (P-01, P-02, P-03, P-04), subplots
(SP-1, Sp-3), and attenuation plots are within 3.5 kilometers
of each other in Oconee County, Georgia (Figure 1). Soils are
predominately Cecil Sandy Loam with high acidity and clay content
and low organic matter.
Schematics of the four watersheds are shown in Figures 2-5,
P-01 is the largest watershed at 2.70 hectares and like P-02
(1.29 ha.) represents poor conservation management practice.
P-03 (1.20 ha.) and P-04 (1.38 ha.) are representative of good
conservation management practice with graded terraces, grassed
waterways, and aerially seeded winter rye crop.
14
-------
SCALE: 1cm ~ 300 METERS
.ATTENUATION PLOTS
• INSTRUMENTATION TRAILER
SP-1
USDA RESEARCH
CENTER
TO ATHENS
TO WATKINSVILLE
Figure I.
Location of experimental watersheds
-------
ELEVATION CONTOURS
SAMPLING ZONE NUMBER
Figure 2.
Schematic of the P-01 watershed (2.70 hectares)
16
-------
ELEVATION CONTOURS
SAMPLING ZONE
NUMBER
Figure 3. Schematic of the P-02 watershed (1.29 hectares)
17
-------
SLOPE 3%
3%
3%
3.2% SLOPE I
ON TERRACE
1 II
SAMPLING ZONE NUMBER
TERRACE
Figure 4. Schematic of the P-03 watershed (1.20 hectares)
TERRACE
GRASS
WATERWA
SAMPLING ZONE
NUMBER
7
3% SLOPE
2%
0.2% SLOPE ON TERRA'CE
Figure 5. Schematic of the P-04 watershed (1.38 hectares)
18
-------
P-01 and P-03 were planted in soybeans and three herbicides
were used: paraquat, diphenamid, and trifluralin. Atrazine and
paraquat were applied to P-02 and P-04, which were planted in
corn. Both subplots were planted in soybeans and paraquat,
diphenamid, and trifluralin were applied. Table 1 summarizes
the field site parameters for 1973 and Table 2 presents the
pertinent herbicide properties.
EXPERIMENTAL PROCEDURE (WATERSHEDS)
The watersheds were primarily designed to provide data on
pesticide movement during runoff producing events. Each water-
shed was equipped with a recording rain gauge. Runoff from the
watersheds was gauged with a 0.762 meter stainless steel H-
flume. During event runoff, samples were collected with a
traversing D.C. powered slot and a stationary splitter. The
runoff sample was allowed to flow by gravity to an adjacent
refrigerated collection compartment. The samples were collected
in 11.35 liter stainless steel beakers positioned on a rotating
platform. All conveyance and collection vessels were fabricated
with stainless steel to prevent pesticide sorption. A float
mechanism was constructed to energize (D.C. power) the rotating
beaker platform at sample completion. Relay circuits were
fabricated with the float device to record the sample collection
time and flume stage height. As described by Fleming and
Leonard, each sample was sub-divided for separate chemical and
sediment analysis. Sediment concentration was determined for
each sample. The chemical analysis involved sediment separation
for pesticide analysis in both the water and sediment
fraction.17'18
19
-------
Table 1.
EPA/USDA FIELD EXPERIMENTAL TEST
SITE DATA FOR 1973
DESCRIPTORS
AREA
NUMBER OF
CORE SAMPLING
AREAS
CONSERVATION
PRACTICE
SLOPE
CROP
PLANT DATE
MATURITY DATE
PESTICIDES
AND
APPLICATION
RATE
CHLORIDE
FERTILIZER
APPLICATION
DATE&
RATE
WASHOUT
REAPPLICATION
DATE/RATE
WATERSHEDS
P-01
2.70 ha
10
-
2-6%
SOYBEANS
JUNE 13, 1973
SEPT 12, 1973
PARAQUAT
1.1 2 kg/ha
DIPHENAMID
3.36 kg/ha
TRIFLURALIN
1.12 kg/ha
(INCORPORATE!
5-10-15
ON
MAY 22, 1973
428 kg/ha
YES
JUNE 4, 1973
500 kg/ha
P-02
1.29 ha
10
2-4%
CORN
MAY 11, 1973
AUG 15, 1973
PARAQUAT
1.1 2 kg/ha
ATRAZINE
3.36 kg/ha
))
YES
6-6-24
ON
MAY 11,1973
640 kg/ha
NO
JULY 23, 1973
112 kg/ha
SIDE DRESSING
P-03
1.20 ha
8
TERRACES
& GRASS
WATERWAYS
P-04
1.38 ha
11
TERRACE
& GRASS
WATERWAYS
3% INTO TERRACE
0. 2% ALONG TERRACE
SOYBEANS
JUNE 15, 1973
SEPT 12, 1973
PARAQUAT
1.1 2 kg/ha
DIPHENAMID
3.36 kg/ha
TRIFLURALIN
1.1 2 kg/ha
(INCORPORATED)
5-10-15
ON
MAY 22, 1973
428 kg/ha
JUNE 4, 1973
500 kg/ha
CORN
MAY 11, 1973
AUG 15, 1975
PARAQUAT
1.12 kg/ha-
ATRAZINE
3.36 kg/ha
YES
SUB-PLOTS
SP-1
9X 22 m
1
-
-
SOYBEANS
JUNE 13,1973
SEPT 12, 1973
SAME AS
P-01
SAME AS
P-01
SAME AS
P01
SAME AS
P-01
SP-3
26 X 39 m
1
-
-
SOYBEANS
JUNE 15, 1973
SEPT 12, 1973
SAME AS
P-03
SAME AS
P-03
SAME AS
P-03
SAME AS
P-03
ATTENUATION
PLOTS
6X9 m
I/PLOT
FLAT
SOYBEANS
JUNE 5, 1973
SEPT 12, 1973
PARAQUAT*
DIPHENAMID*
TRIFLURALIN'
(INCORPORATED)
5-10-15
448 kg/ha
•FOUR PLOTS WERE CONTROL WITH NO HERBICIDE APPLICATION,
FOUR APPLICATIONS WERE THE SAME AS P-01 AND P-03, AND
FOUR APPLICATIONS WERE AT ONE-HALF THE P-01 AND P-03 RATES.
20
-------
Table 2.
PROPERTIES OF HERBICIDES APPLIED ON
EPA/USDA TEST SITES
HERBICIDE
2-CHLORO-4-IETHYLAMINO) C1
-6-dSOPROPYLAMINOI Jl
-S-TRIAZINE ^^^\
N f$
,'' "->
\ I
\ /
H3C
C/N": ATRAZINE H3C' H "^ N -xNHC2Hg
T/N: AATREX80W
M/F: C8H14CIN5
N.N - DIMETHYL-2,2 DIPHENYLACETAMIDE
/7^\
H3c rvv ,'/
\ ° 1 ^^^
N-C -CH
3 (' ^^\
C/N: DIPHENAMIDE \\ ,' /
T/N: ENIDE
M/F: C16H17NO
4, 4'-BIPYRIDYLIUM-2A, I.I'-DIMETHYL
DICHLORIDE
Lc-N
-------
After a runoff event, soil core samples were collected from
each watershed to determine the pesticide distribution in the
soil profile and to provide mass balance information. Based upon
the size of the area, soil properties and slope, sampling units
were identified for each test site. A composite sample for each
unit was obtained by combining 12-15 discrete samples and mixing.
Each of the core samples were subdivided into seven depth incre-
ments as follows: 0-1, 1-2.5, 2.5-5.0, 5.0-7.5, 7.5-15, 15-22.5,
and 22.5-30 cm.
EXPERIMENTAL PROCEDURE (ATTENUATION PLOTS)
The smaller attenuation plots (6x9 meters) located near
the P-03 and P-04 watersheds were highly instrumented to provide
detailed data on pesticide attenuation and degradation between
runoff events. A PDP8/E minicomputer system housed in an air
conditioned trailer was programmed and interfaced to sensors
providing data on wind speed, wind direction, solar radiation,
relative humidity, air temperature, rainfall, soil temperature,
and soil moisture (Table 3). During operation some 53,000 data
points were collected and stored on magnetic tape each day. In
addition to the automated environmental data, manual systems
were employed to collect information on evaporation, rainfall,
runoff, sediment loss, and soil moisture content (gypsum block
and gravametric).
A stainless steel catchment trough was established at the
base of each of the six center plots to collect surface runoff.
Runoff from the plots flows by gravity to the collection facility.
Runoff coming from the trough moves through a five-to-one
splitter into a large holding tank. When this tank is full,
overflow is further divided by a ten-to-one splitter. Spill-
over from this divisor goes to a second holding tank. The total
22
-------
Table 3. ENVIRONMENTAL PARAMETERS RECORDED WITH
THE PDP8/E DATA ACQUISITION SYSTEM ON
SIX OF THE ATTENUATION PLOTS
PARAMETER LOCATION (cm)
Wind Speed 30.48, 121.9, 304.8
(3 Heights)
Wind Direction 121.9, 304.8
(2 Heights)
Solar Radiation 182.9
(Up and Down)
Relative Humidity 30.48, 121.9
(2 Heights)
Air Temperature 2.54, 61.0, 121.9, 304.8
(4 Heights)
Rainfall
(Tipping Bucket)
Soil Temperature 0.0, 1.0, 2.54, 5.08, 15.24,
(7 Depths) 22.86, 60.96
Soil Moisture 5.08, 10.16, 15.24, 22.86,
(5 Depths) 38.1
23
-------
collecting system's capability is eight inches of runoff. A
representative sample was taken from each tank for pesticide
analysis in both the water and sediment fraction.
The following sections utilize some of the experimental
data (described above) collected by EPA/USDA to test the sub-
models which are presently incorporated into the SCRAM simula-
tion structure.
24
-------
SECTION V
SIMULATION STRUCTURE
INTRODUCTION
Simulation is the development and use of models to aid in
the evaluation of ideas and to study dynamic systems or situa-
tions. A model of a system is anything that is employed to
represent the system for some set of purposes. Parts of a sys-
tem (components) are often regarded as systems or subsystems of
the larger system. Thus models which represent subsystems may
be referred to as submodels or models if the context is clear.
Models can be divided into three classifications:
(1) models which seek to describe the environment in real terms
are categorized as "deterministic," (2) "stochastic" models, which
incorporate the concepts of risk, probability, and other measures
of uncertainty, and (3) "optimization" models, which find the best
possible solutions subject to specified constraints.
Deterministic models may be based upon mathematical equations
which describe the underlying physical processes. Alternatively,
the mathematical equations may be developed empirically- For
example, a model used to describe movement (infiltration) of
water through the soil surface into the soil profile may start
with a differential equation describing fluid flow in a non-
deformable media. The solution to the differential equation
becomes the infiltration model. By comparison, an empirical
model might simply assume that the infiltration rate is inversely
proportional to the cumulative infiltration.
25
-------
SCRAM was developed to simulate the movement of pesticides
from agricultural lands to the aquatic environment. Submodels
are based upon "first principles"; empiricism is avoided except
where knowledge of basic laws is insufficient or the simplifica-
tion is consistent with project objectives. The choice of models
based upon first principles does not imply that these models are
always superior to empirical models. However, simulation of
pesticide transport based upon empirical models has been described
19
elsewhere and therefore is not a concern of this study.
SIMULATION DESIGN
SCRAM has been designed to provide maximum flexibility for
the user. Two features provide this flexibility: the division
of the watershed into zones, and the modular nature of the
simulation structure.
An important aspect of SCRAM"s organization is the provision
for watershed zones or subplots. At the present time a unique
zone is defined within the watershed if it has uniform topo-
graphical features, the same soil type, or the same rainfall
rate. As part of the simulation input the user must specify the
soil parameters, slope, and rainfall data for each zone. In
addition it is necessary to specify how runoff water moves among
zones.
SCRAM was designed around a modular format to facilitate
the addition of new models for processes not presently modeled
and to allow users to substitute and test alternative models for
existing models. To the extent possible, each component of the
system being modeled is programmed and coded in a separate sub-
routine. External environmental parameters are stored in a common
area of the computer which is accessible to all of the subrou-
tines. Internally generated parameters are also transferred to a
common area for access by other subroutines.
26
-------
The simulation is under the control of an executive program,
the Master Scheduler, which schedules and calls all of the sub-
routines. At the present time SCRAM contains two operational
routines and seven functional routines in addition to the Master
Scheduler. Operational programs control the input and output
during the system simulation. The functional programs correspond
to the physical processes of evapotranspiration, water movement,
sediment transport, pesticide degradation, pesticide adsorption
in the soil profile, pesticide volatilization, and pesticide mass
balance (see Figure 6).
A discussion of each of the major programs and associated
subroutines follows. Additional details are contained in Section
VII and the documentation and program listings in the appendices.
The potential application of SCRAM to large watersheds is
discussed in the last part of this section.
MASTER SCHEDULER
The Master Scheduler determines the time sequencing of the
simulation. By defining the time sequencing of the simulation,
the Master Scheduler controls all of the interrelationships among
the functional subroutines. Any modification to these relation-
ships or any addition to the set of functional subroutines
would require alterations in the Master Scheduler. For example,
in the present structure, the evapotranspiration functional
subroutine, EVAP, is not called during periods of rainfall or
immediately after rainfall ceases. If the user decided to acti-
vate evapotranspiration immediately after rainfall ceases,
changes would be made to the Master Scheduler, not the EVAP
subroutine.
The Master Scheduler initiates and terminates the simulation
at user specified times. After starting the simulation, the
Master Scheduler calls the input subroutines to read all
27
-------
Figure 6.
Flowchart of the master scheduler
(simplified version)
28
-------
necessary and available data. It then cycles through functional
subroutines according to the environmental conditions being
simulated. At present, SCRAM includes a water cycle and a
pesticide cycle. After each complete cycle, BALANC, the book-
keeping subroutine, is called. The Master Scheduler then
calculates a new simulation time increment, DT, and repeats the
cycle among the functional subroutines and BALANC. At user
selected intervals, the Master Scheduler calls the output
routines to print intermediate results. When the Master
Scheduler ascertains that the stop time has been reached, it
calls the output routines selected by the user and ends the
simulation.
INPUT ROUTINES
Several input subroutines are included in SCRAM to handle
the different types of data and the variable startup conditions.
During initial startup, simulation input is read from a card
reader and stored on disk files. Thereafter the system may
be restarted from the disk files. The major input subroutines
are associated with reading rain gauge cards, environmental data
cards, and simulation parameter cards.
SEQDAT reads all of the rain gauge cards, checks for format
errors (calls ERROR), calculates the rainfall rate between rain
gauge readings, and writes the rainfall history and rainfall
rates onto a disk file. SEQDAT also reads the environmental data
on wind speed, temperature, solar radiation, atmospheric pressure,
and relative humidity for storage on a disk file.
After SEQDAT, INPUT is called to read all of the simulation
parameters (namelist data), including the soil pressure head
•nd diffusivity tables, watershed zonal definition or subplot
lineation, and pesticide adsorption-desorption parameters.
29
-------
All units are converted to the metric system for internal use.
Finally, INPUT sets up the simulation start and stop times. If
the "warm start" option is utilized, INPUT detects this option
and sets up the simulation.
After INPUT, DATINT is called to make the final preparations
for starting the simulation. DATIN is called to read the appro-
priate rainfall cards into common storage. DATEPA reads the
appropriate environmental cards into common.
DATEIN is a special routine called by any of the input
routines which contain year, month, day, and clock time. All
conventional dates are converted to the standard computer Julian
time for internal use.
OUTPUT ROUTINES
The output routine provides printed, punched, and disk
storage output to the user. The output subroutines are DATOUT,
ERROR, OUTPLT, OUTPUT, PRINTH, and SETUP.
DATOUT calculates the calendar date from the Julian date
and writes both dates on each printout specified by the user.
ERROR is the output subroutine that prints one of the
following error messages and terminates the simulation:
ERROR =
ERROR =
ERROR =
ERROR =
ERROR =
ERROR =
ERROR =
1
2
3
4
5
6
7
input date error
time interval error
rainfall input data error
zone definition error
soil type number > 10
input temperature error
runoff definition error.
30
-------
OUTPLT produces printer plots on standard line printers
for SCRAM. Presently, six plots are produced which are related
to runoff and sediment loss from the watershed:
• total runoff (liters) vs time (sec)
• runoff rate (liters/sec) vs time (sec)
• runoff/total rain (percent) vs time (sec)
• sediment rate (kg/hr/hectare) vs time (sec)
• sediment load (kg/hectare) vs time (sec)
• sediment/runoff (kg/liter) vs time (sec).
A punched card option is included to produce card images of the
printer plot data on runoff rate (liters/min) vs elapsed time, and
sediment loss (kg/min) vs elapsed time. The punched cards were
used to generate CALCOMP plots for the major storms.
OUTPUT is the major simulation output subroutine. At user
specified time intervals it prints the state of the system. At
the specified time interval, state information is printed on the
line printer as follows:
• watershed identification data
• date and time
• rainfall rate
• soil moisture profile for each watershed
zone down to 15 cm.
• cummulative infiltration
• pesticide distribution in the soil profile
• runoff rate for each zone and at the confluence
of the watershed
• rate of sediment loss for each zone and at
the confluence of the watershed
31
-------
• accumulated runoff for each storm
• accumulated sediment loss for each storm
• instantaneous pesticide loss in the runoff
• instantaneous pesticide loss on the sediment
• accumulated pesticide loss in the runoff
• accumulated pesticide loss on the sediment
• evapotranspiration water loss.
If print intervals are not specified, the default value is every
simulation time increment. OUTPUT also prints card images of the
input data set.
SETUP is a specialized output routine which prints the ESL
logo at the beginning of the simulation as an identifying symbol.
BOOKKEEPING
BALANC is SCRAM's bookkeeping subroutine. Its function
is to move runoff water and sediment between watershed zones and
keep a mass balance on the pesticide. BALANC is called at the
end of every time increment before the print routines are called.
Results from the BALANC subroutine are used as input to the next
cycle through SCRAM.
BALANC moves the runoff produced in every time increment
from the originating zone onto neighboring zones, according to the
watershed parameters specified by the user. The present structure
allows runoff from one zone to move onto a maximum of four
adjacent zones. This water movement is limited by a maximum run-
off rate which is another watershed parameter supplied to the
simulation. Sediment is distributed exactly like the runoff.
32
-------
Pesticides are moved according to the distribution of
runoff and sediment. When this is done, BALANC performs a mass
balance on the amount of pesticide in the upper soil layers and
in the runoff and on the sediment. In this way, pesticide mass
is conserved.
BALANC also performs a mass balance on the amount of water
in the simulation system. This is done by comparing the total
amount of water entering the system (rainfall) with the total
amount in the system (infiltration and storage) and leaving
the system (evapotranspiration). This comparison is one of the
printout options available to the user.
THE WATER CYCLE
The water cycle (Figure 7) is the major sequence called
by the Master Scheduler. During periods of rainfall the infiltra-
tion-percolation functional subroutine, WATER, is called. When
runoff is generated the sediment functional subroutine, SED, is
called. The evapotranspiration function subroutine, EVAP, is
called under user specified conditions.
Presently, the WATER and EVAP (evaporation and transpiration)
subroutines are mutually exclusive in the simulation structure.
The reasons for this are complex but are basically related to
simulation constraints and limitations of the pesticide
adsorption-desorption model. During periods of evaporation,
transpiration, and percolation, the concentration of pesticide
in the soil profile is being changed in a variety of ways. At
the same time the pesticide degradation model degrades adsorbed
and dissolved pesticide. The adsorption-desorption model cannot
handle this combination of changes and at the same time conserve
pesticide mass.
33
-------
Figure 7. The water cycle
34
-------
To get around the problem the user must specify a thres-
hold moisture content for the soil surface. When the soil
moisture content drops below the threshold, WATER is no longer
called. Because EVAP functions by removing soil moisture starting
at the top soil layer, the potential error associated with this
procedure is minimized.
The WATER functional subroutine is based on the Darcy
continuity equation and is discussed in detail in Section VII.
WATER predicts the infiltration rate, soil moisture profile, and
runoff rate for each watershed zone. The velocity of water move-
ment between soil layers is stored in a common area for use by
the adsorption-desorption model. The soil moisture profile is
also stored in common for use by the pesticide degradation,
volatilization, and evapotranspiration models.
The parameters presently required by WATER include: initial
soil moisture profile, rain gauge data for each watershed zone,
and the pressure head and soil diffusivity tables for each soil
type specified for a particular watershed zone. If the soil
parameters are not known, the tables in Section VII can be used to
develop reasonable tables for the simulation.
SED is the sediment functional subroutine. Its function
is to predict the amount of sediment washed off each watershed
zone during a runoff event. This quantity is also directly
related to the movement of pesticides. SED is called every
simulation time increment for each zone that has runoff water.
Several input values are required by the SED functional
subroutine. Presently, the SED functional subroutine receives an
input rainfall intensity from the input rainfall history, input
watershed parameters, sediment model parameters, and total amount
of runoff moved off each subplot during the time increment which
35
-------
is calculated by the WATER functional subroutine and distributed
by BALANC. The only output requirement of the SED subroutine
is the sediment load at the bottom of each subplot for each time
increment.
SCRAM presently employes a modified Foster-Meyer sediment
model as the basis for the SED functional subroutine. It is
sensitive to slope, depth of runoff, and indirectly, to crop cover,
The Foster-Meyer sediment model is fully described in Section VII
of this report.
EVAP is the evapotranspiration functional subroutine.
It determines potential evapotranspiration for each time increment.
Other related subroutines determine the actual water loss
depending on the cloud cover, relative humidity, time of year,
and ground cover. Moisture is extracted from the soil profile
beginning at the top layer and continuing down through successive
layers until a user specified depth is reached. The minimum
moisture content in a given soil layer is never reduced below the
minimum value in the tables of pressure head and diffusivity
specified by the user.
EVAP is called when the rainfall rate is zero and the
soil moisture content of the first soil layer (usually one centi-
meter) is below a user specified threshold (typically 0.3 to
0.4 centimeters, but the specified value depends on the soil
type). As noted above EVAP and WATER are mutually exclusive.
Several input values are presently required by the EVAP
functional subroutine. They are meteorological data, watershed
latitude, and vegetation ground cover. The sole output require-
ment for the EVAP functional subroutine is the potential evapo-
transpiration available for each time increment.
EVAP is presently based on a modified Penman equation
which is fully described in Section VII.
36
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THE PESTICIDE CYCLE
The other major cycle within SCRAM's simulation structure
is the pesticide cycle. This cycle introduces the pesticide into
the simulation and accounts for all the physical processes in-
volving the pesticide during the simulation. The present cycle
includes an adsorption-desorption functional subroutine, a
degradation functional subroutine, and a volatilization
functional subroutine. The pesticide is introduced and dispersed
in the soil profile by the adsorption-desorption functional sub-
routine. The degradation and volatilization functional sub-
routines remove pesticide from the soil profile. Figure 8 shows
a simplified flowchart of the pesticide cycle.
The pesticide cycle is dependent on the water cycle for
infiltration rate, water velocities in the soil profile, and
the soil moisture profile for each watershed zone. Both cycles
are called within the same simulation time increment (simu-
ltaneously) .
ADDE is the adsorption-desorption functional subroutine.
ADDE introduces the pesticide into the soil profile and moves the
pesticide into the soil profile according to its adsorptive-
desorptive properties. The pesticide concentration in solution
and adsorbed is calculated for each soil layer and each watershed
zone.
Introduction of the pesticide in the soil matrix occurs
during simulation of the first rainfall event after pesticide
application. The pesticide is moved vertically into the soil
profile in the solution state in the direction of the net moisture
flux. Once the pesticide is in a soil layer, adsorption occurs.
The continual movement of moisture throughout the soil profile,
due to infiltration, percolation, evaporation, and redistribution
transports the solution phase of the pesticide while the continued
37
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Figure 8.
SCRAM pesticide cycle
adsorption-desorption process simultaneously occurs. The con-
tinuous relationship between the adsorbed state and the dissolved
state is generally expressed as a Freundlich relationship.
Soil bulk density, soil water flux between soil layers,
pesticide solubility, pesticide adsorption and desorption
coefficients, a pesticide diffusion coefficient, and a pesticide
conductively parameter must be available to ADDE. The WATER
functional subroutine supplies soil water flux. The remaining
parameters must be specified by the user.
At the present time ADDE is based on a dynamic adsorption-
desorption model described by a one-dimensional differential
equation. The adsorption-desorption processes are described
by Freundlich equations. The fundamental equations are described
in Section VII. Modifications were made to interface ADDE with
WATER and account for the processes of evapotranspiration and
pesticide degradation.
38
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DEGRAD is the pesticide degradation subroutine. Its purpose
is to account for the degradation of the pesticide in the soil
profile. This degradation process has been shown to be dependent
on soil moisture and soil temperature.
The input values required by DEGRAD are watershed parameters,
soil properties, volumetric soil moisture content supplied by the
WATER functional subroutine, and the soil temperature profile.
The output required from DEGRAD is a multiplicative degradation
factor to be used by BALANC, the bookkeeping subroutine, to
degrade dissolved and adsorbed pesticide. An adequate DEGRAD
functional subroutine should calculate a single multiplicative
factor for the entire profile, whereas an ideal model should
calculate depth dependent degradation factors corresponding to
the depth dependent values of soil moisture and temperature.
DEGRAD is presently based on a first-order differential
equation which describes subsurface pesticide degradation as a
function of soil moisture and temperature.
VOLT is a specialized functional subroutine which is called
only if the pesticide is known to be highly volatile. At the
present time DEGRAD and ADDE are not called when VOLT is called.
Two options are provided according to whether the pesticide
diffusion coefficient is known or to be calculated from a linear
regression equation based upon soil moisture, temperature, and
bulk density.
VOLT requires input data on the pesticide application
rate, initial pesticide distribution in the soil profile, soil
bulk density, and the pesticide diffusion coefficient. If the
diffusion coefficient is calculated, the WATER program supplies
soil moisture profiles and the soil temperature profile is
presently taken as constant.
39
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VOLT is based upon solutions to the standard second order
differential equation of diffusion (Pick's Second Law). Modifica-
tions and approximations were made to account for nonuniform
incorporation of pesticide and interlayer diffusion. Details of
the mathematical formulations are in Section VII.
SIMULATING LARGE WATERSHEDS
Approaches
SCRAM was originally designed to simulate pesticide transport
on small watersheds of less than five hectares. However, during
the second phase of the project, the simulation structure was
drastically modified to provide greater flexibility and potential
application to large water basins. The essential feature of the
change is the introduction of watershed zones or subplots into
the simulation structure to allow for areal variations in soil
type, rainfall rate, and topography.
Two approaches were considered. The first was statistical
and involved assigning probability distributions to the rainfall
rate and infiltration capacity over the watershed area. The
second approach associates unique combinations of soil properties,
topography, and meteorological data with each zone. The first
approach requires very little additional programming and minimal
additional computer core storage. The second approach requires
significant additional programming and large amounts of additional
core storage. In addition, program execution time increases in
proportion to the number of zones. In keeping with the basic
SCRAM approach to avoid empirical and statistical models, the
second approach was implemented.
40
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WATERSHED ZONES
A maximum of 20 zones or subplots may be specified for a
watershed. On small watersheds each subplot should have homo-
geneous soil properties and uniform topographic characteristics.
Ordinarily the subplots all have the same rainfall rate and areal
variation in rainfall is not required. The user is required
to define the runoff relationship among the subplots, i.e., the
distribution of runoff water from each subplot to adjacent sub-
plots. Although primarily designed for simulating large water-
sheds, this procedure was used to simulate the runoff from the
EPA/USDA watersheds (<3 hectares).
Expanding the subplot concept to a larger watershed, the
user would divide the watershed into a maximum of 20 zones. Each
zone would have a unique rainfall history, soil hydrologic
properties, meteorology, and topography. As was the case for
small watersheds the user defines the runoff relationship among
the zones.
Even though the concept of zones has been introduced,
some uniformity over the entire watershed is still required.
The data needed for the total watershed is:
• Crop information
a) crop type
b) plant date
c) maturity date
d) harvest date
• Pesticide data
a) pesticide properties
b) application rate
41
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c) application method
d) application date.
Data for each zone is permitted for:
• Rainfall history
• meteorology
a) temperature
b) relative humidity
c) wind velocity
d) cloud cover
e) barometric pressure
• Soil parameters
a) soil type
b) hydraulic conductivity or diffusivity
c) pressure head
• Average slope.
DATA REQUIREMENTS FOR WATER BASIN TESTING
In addition to the watershed zonal information specified
above, a minimal experimental data set is required with:
• Measured runoff rate and volume for a single
runoff event
42
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• Measured sediment loss for the same runoff event
• Measured pesticide concentrations in the runoff
and sediment.
Data for a complete growing season, rather than a single rain
event, would be desirable.
Efforts to establish a suitable data base with which to test
SCRAM included a literature search and attempts to acquire
unpublished data.
The literature search failed to disclose a single data
base possessing all the parameters required to test SCRAM.
In the search for unpublished data, inquiries were made
to several offices of the United States Department of Agriculture,
Agricultural Research Service. While portions of the required
data were available, notably from the South Great Plains Watershed
Laboratory in Chickasha, Oklahoma, a complete data set was
unavailable. To realistically assess the water basin capabilities
of SCRAM a complete data set is required. Simulation based on an
incomplete data base would be costly without providing meaningful
information.
43
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SECTION VI
SIMULATION TESTING
INTRODUCTION
The testing of any complex simulation like SCRAM is a
difficult process because of the interdependencies between sub-
models. For example, if the runoff is incorrectly predicted the
sediment loss should also be incorrect. If both the sediment and
runoff models are incorrect the error in predicting sediment loss
may be compounded. Similarly, if the runoff is incorrect too
much or too little water is infiltrated. The adsorption-desorp-
tion and degradation models depend on the amount of water infil-
trated. Pesticide loss in the runoff and on the sediment depends
on the runoff model, the sediment model, the adsorption-desorp-
tion model, and the degradation model. These relationships must
be kept in mind when testing the simulation and interpreting the
results.
Testing a simulation based upon deterministic submodels,
which purport to describe the underlying physical processes, is
somewhat different than testing a simulation designed around
empirical or statistical models. The distinction lies in the
way the simulation parameters are determined. Statistical and
empirical model parameters are determined by "calibrating" the
simulation against large masses of field experimental data. This
procedure is somewhat akin to curve fitting and least squares
analysis. As long as the number of parameters exceeds the
number of variables by a sufficient margin, good results are
reasonably assured.
44
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SCRAM utilizes deterministic models based upon scientific
principles. In theory, the model parameters can be determined
independently, usually in a laboratory experiment, and then used
in the simulation. Thus, the soil properties, pressure head and
diffusivity, pesticide adsorption-desorption parameters, and the
pesticide degradation parameters could be determined from
laboratory experiments. For some models such as the sediment
model this is not true. And of course the laboratory may be
the field test site. If the simulation does not produce good
results, the implication is that something is wrong with the
appropriate underlying model rather than the simulation para-
meters. The first adjustments should be made to the model itself
and only as a last resort should the parameters associated with
the model be changed.
It was not possible to test SCRAM against all of the EPA/
USDA field data as described in Section IV. Two watersheds,
P-01 (2.70 hectares; non terraced, soybeans) and P-04 (1.38
hectares, terraced, corn) were selected for testing because
of their relative sizes, locations, and crops. Diphenamid
(P-01) and atrazine (P-04) were selected as test pesticides.
Paraquat does not need to be simulated because it is strongly
adsorbed on sediment and hence the sediment model determines
the paraquat loss. A third pesticide, trifluralin, was used to
test the volatilization model.
The results of the simulation tests are described in the
remainder of this section. Runoff results (hydrographs) are
presented first, followed by sediment loss, pesticide loss in
the runoff and on the sediment, pesticide movement in the
soil profile, pesticide degradation, and pesticide volatilization,
The simulated results are compared to field measurements for the
45
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major runoff producing storms. However, the entire period
from plant date through December 31, 1973, was simulated as
a single four hour run on an IBM 370/145.
HYDROGRAPHS
In order to simulate the runoff from a small watershed using
SCRAM, the user must specify the soil parameters by providing
tables of moisture potential and diffusivity as a function of
soil moisture content. Because of the approximations contained
in the water model (e.g., soil depth increment, time step,
boundary conditions), experimental values of moisture potential
and diffusivity may not be an optimum choice. Selection of the
parameters is also complicated by the requirement that the evapo-
transpiration model work properly if runoff is to be accurately
predicted.
The predominate soil type in the area of the experimental
watersheds is Cecil Sandy Loam, a typical Hapludult. However,
based upon the results of the sensitivity analysis (Section VII),
it was clear that the diffusivity and moisture potential data
on Cecil Soils would not produce runoff for the storms recorded
during 1973. Because of this and the limited availability of
good hydrological data for a broad range of soil types, the
initial simulation testing was accomplished using parameters
for Light Clay (Section VII, Figures 65 and 66).
The hydrographs have been plotted against elapsed time
rather than real time as recorded during the field measurements.
Elapsed time is measured from the start of runoff. By plotting
the hydrographs as a function of elapsed time differences which
are due to clock asynchronization between the rainfall gauge
46
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and the hydrograph record are minimized. Also, differences
between experimental and simulated hydrographs which are due
to watershed characteristics which were not simulated are
eliminated.
P-01 Hydrographs
The first storm of interest on P-01 occurred on the plant
date, June 13, 1973. This storm is one of the most unusual
storms recorded. Rainfall rates exceeded 0.2 cm/min; 1.6 cm of
rain fell in the first 7 minutes of the storm. The rain stopped
for 15 minutes during the storm, and a total of 1.9 cm was
recorded in 26 minutes.
Simulated and actual hydrographs for June 13, 1973, are
shown in Figure 9. The simulated hydrograph reflects a much
faster response to the 1.6 cm of rainfall during the first 7
minutes of the storm. Most of the simulated runoff (335,297
liters) is caused by the fact that the rainfall rate exceeded the
maximum infiltration rate permitted in the infiltration model.
Measured runoff was 369,445 liters or 72% of the total rainfall,
a surprisingly high figure in light of the recent tillage and
dry soil conditions.
The second major storm on P-01 occurred on June 21,
1973. This storm was entirely different from that on June 13,
1973. Light rain for 8 minutes was followed two hours later by
a twenty minute burst (1.4 cm), and then light rain for 10
minutes (0.1 cm).
The actual hydrograph shows a response only to the 20 minute
peak rainfall, whereas the simulated hydrograph shows a response
both to the rainfall peak and the light rainfall following the
peak (Figure 10). The shape of the measured hydrograph compared
to the measured hydrograph for June 13, 1973, illustrates the
47
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ELAPSED TIME (MIN)
88
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Figure 9.
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P-01 watershed: hydrograph for the
June 13, 1973, storm
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MEASURED(112,397 LITERS)
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ELAPSED TIME (MIN)
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Figure 10.
P-01 Watershed: hydrograph for the
June 21, 1973, storm
48
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initial changes that have occurred due to channelization and
compaction. The six minute burst of rain on June 13 produced
40 minutes of runoff while the 20 minute peak rainfall period
of June 21 produced runoff for less than 30 minutes. This
effect is not simulated and the difference is not observed.
Total measured runoff was 112,397 liters or 22% of the total
rainfall. Expressed as a percentage of the 1.3 cm peak, 32%
was observed as runoff. Simulated runoff was 183,487 liters
(36%).
On July 8, 1973, 1.8 cm of rain fell over a period of 96
minutes. The rainfall rate decreases from the beginning of the
storm (.05 cm/min) to the end of the storm (.007 cm/min). Hence,
the high intensity rainfall of June 13, 1973, and June 21, 1973,
is not present.
The actual hydrograph has two peaks of nearly equal magni-
tude, whereas the simulated hydrograph has a single peak of much
smaller intensity (Figure 11). Actual runoff was 132,821 liters
(27%) versus 32,938 liters (7%) simulated. Given the absence of
two peaks in the rainfall record it is difficult to reconcile
the measured hydrograph with the simulated hydrograph. Crop
canopy may begin to impact on the form of the hydrograph at this
time but the effect would be to eliminate peaks or smooth out
the hydrograph. (The P-04 hydrograph for July 8, 1973, has two
peaks, but the rainfall record also has two peaks.)
On July 30, 1973, a total of 2.8 cm of rain fell in 30
minutes. The actual hydrograph has an unusual flat top at the
peak flow for 8 minutes. Total measured volume1 was 354,674 (47%)
vs simulated volume of 457,400 (61%) (see Figure 12). At this
point crop canopy may begin to reduce runoff volume, but the
magnitude of the difference suggests that the soil type is not
appropriate.
49
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4840
3630
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MEASURED(132,821 LITERS)
22
44 66
ELAPSED TIME (MIN)
110
Figure 11.
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P-01 watershed: hydrograph for the July 8,
1973, storm
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ELAPSED TIME (MIN)
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Figure 12
P-01 watershed: hydrograph for the July 30,
1973, storm
50
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The next big storm did not occur until Sept 9, 1973, when
4.1 cm fell over a period of 91 minutes. Simulated runoff
(641,508 liters, 58%) again exceeds the recorded volume (400,461
liters, 36%) as shown in Figure 13. Based upon the form of this
hydrograph and previous ones, the soil parameters for clay do
not provide sufficiently rapid percolation once the surface has
saturated.
On Sept 13, 1973, 1.0 cm of rain fell over a period of 110
minutes, followed by 108 minutes without rainfall, and then 2.0
cm of rain fell over 39 minutes. The first 1.0 cm of rain did
not produce any runoff. Both hydrographs have the same shape
(Figure 14), but the simulated runoff of 286,226 liters (53%)
exceeds the measured runoff of 224,742 liters (42%).
The largest discrepancy between simulated and observed run-
off occurred for the storm on December 5, 1973, (Figure 15).
Simulated runoff was 458,169 liters (42%) whereas measured run-
off was only 21,360 (2%). Part of the difference is due to the
small amount of rain that fell on December 4, 1973, late at
night, which is not adequately handled in the present structure.
However, at best this could only increase the runoff by 54,000
liters.
An examination of the rainfall rates does not produce an
explanation. Rates in excess of 0.06 cm/min were observed during
two periods (first two peaks in the simulated hydrograph) follow-
ed by a rate greater than 0.02 cm/min (third peak in simulated
hydrograph). Rates less than these produced substantial runoff
during other storms.
The final storm of the calendar year occurred on December
31, 1973. This storm came 14 hours after a storm on December 30,
1973, of 2.3 cm. Although the shape of the hydrographs (Figure
16) are in excellent agreement, the simulation using clay para-
meters predicts 657,600 liters (49%) of runoff whereas the mea-
sured runoff was 478,382 liters (36%).
51
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SIMULATED (641,508 LITERS)
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ELAPSED TIME (MIN)
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1973, storm
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SIMULATED (286,226 LITERS)
MEASURED(224,742 LITERS)
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ELAPSED TIME (MIN)
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Figure 14
P-01 watershed: hydrograph for the September
13, 1973, storm
52
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Figure 15.
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132
165
ELAPSED TIME (MIN)
hydrograph for the December 5,
P-01 watershed:
1973, storm
SIMULATED (657,600 LITERS)
MEASURED(478,382 LITERS)
110
220 330
ELAPSED TIME (MIN)
440
550
Figure 16.
P-01 watershed:
1973, storm
hydrograph for the December 31,
53
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The four hour simulation run covering the period from June
13 through December 31, 1973, included a large number of smaller
storms in addition to the eight major events discussed above.
No particular pattern was evident from examining these storms.
Most produced no runoff either simulated or measured. Some pro-
duced simulated runoff below measured. Total simulated runoff
for the period June 13, 1973, through December 31, 1973, was
3,372,866 liters, whereas the recorded runoff was 2,179,497
liters.
The difference between total simulated runoff and recorded
runoff could be eliminated by adjusting the soil parameters.
However, the selection of total runoff as an optimization cri-
terion is, at best, only appropriate for the infiltration model.
For purposes of predicting the amount of pesticide washed off of
P-01 for the season, it would be optimum to adjust the soil para-
meters to increase the runoff simulated for the June 13, 1973,
storm. It would only be slightly more difficult to adjust the
soil parameters to match the June 13, 1973, storm and improve
the match between total simulated runoff and recorded runoff.
P-04 Hydrographs
In order to compensate for the overprediction of runoff on
P-01 using moisture potential and diffusivity for Clay and for
comparative purposes, soil types were changed before simulating
the P-04 watershed. Essentially, hybrid soil was constructed by
combining the moisture potential data for Clay with diffusivities
for Geary Silt Loam. To simplify notation the hybrid is called
SERL loam. Again, the necessity for the hybrid soil rather than
Cecil Soil is apparent from the sensitivity analysis in Section
VII of this report and from Figures 65 and 66 of that Section.
54
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The first runoff producing storm after planting on P-04
occurred on May 23, 1973. It was a small storm of 1.2 cm, occur-
ing over a period of 167 minutes. Simulated runoff was 6365
liters which exceeded the measured runoff of 2609 liters (Figure
17). This difference is not particularly significant because
less than 2% of the rainfall was runoff.
On May 28 ^ 1973, two large storms occurred on P-04. During
the morning 4.8 cm fell over a period of 138 minutes. During
late afternoon 4.3 cm fell over a period of 319 minutes. Simu-
lated runoff shown in Figures 18 and 19 was below measured runoff
for both storms. The shape of the simulated hydrograph for the
morning storm is in excellent agreement with the measured hydro-
graph but does show a more pronounced response to the three peak
rainfall periods. In the afternoon, the simulated hydrograph has
three peaks whereas the measured hydrograph has four. However,
the rainfall record for this storm reveals only three peaks and
the fourth peak in the measured hydrograph is a mystery-
On June 6, 1973, 3.9 cm of rain fell over a period of 129
minutes. Simulated runoff was 241,810 liters vs measured runoff
of 280,593 liters (Figure 20). The faster response to changes
in the rainfall rate can again be seen in the simulated hydro-
graph. The spike at 44 minutes is reflected in the rainfall
record but is not noticeable in the measured hydrograph.
The largest storm of the season occurred July 8, 1973, when
6.4 cm fell over a period of 231 minutes. This time the simulat-
ed runoff (464,050 liters) exceeded the measured runoff (411,185
liters). The sharp peaks in the simulated hydrograph shown in
Figure 21 follow the sharp peaks in the rainfall record.
55
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hydrograph for the May 23,
SIMULATED (263,700 LITERS)
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Figure 18,
P-04 watershed: hydrograph for the May 28,
1973, storm
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P-04 watershed: hydrograph for the May 28,
1973, storm (PM)
SIMULATED (241,810 LITERS)
MEASURED (280,593 LITERS)
Figure 20,
22
44 66
ELAPSED TIME (MIN)
110
P-04 watershed: hydrograph for the June 6,
1973, storm
57
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During July and August there were a number of small storms
but most of them did not produce any runoff. Significant runoff
does not occur again until September 9, 1973, when 4.4 cm fell
on P-04 over a period of 108 minutes. Simulated runoff of
226,900 liters exceeded measured runoff of 163,449 liters and
the simulated hydrograph shows a dramatic response to a 20 minute
lull in the rainfall rate (Figure 22).
The best agreement between simulated runoff (130,700 liters)
and measured runoff (132,777 liters) was recorded for the Septem-
ber 13, 1973, storm. Characteristically, the simulated hydro-
graph shows a sharp response to the burst of rainfall that occur-
red late in the storm (Figure 23).
Between September 13, 1973, and December 5, 1973, a number
of small storms were recorded which did not produce any measured
or simulated runoff. On December 5, 1973, 3.9 cm of rain fell
over a period of 452 minutes, but most of the rain was concentra-
ted in a 200 minute period. Simulated runoff (52,000 liters)
exceeded measured runoff (11,016 liters) and the simulated hydro-
graph shows a sharp response to the three bursts of rainfall
which were recorded (Figure 24). This storm was equally trouble-
some on P-01 and the results suggest that there is something
unusual happening.
The final big storm of the year occurred on December 31,
1973, and extended into the morning hours of January 1, 1974.
For approximately two hours it rained lightly, then for 38
minutes it rained at a moderate rate and then it drizzled for
9-1/2 hours. Simulated results do not agree with the measured
results (Figure 25). The measured hydrograph shows runoff for
the entire storm, whereas the simulated hydrograph does not show
any runoff during the light drizzle. Rainfall rates of 0.004
cm/min recorded for this storm did not produce runoff during
the summer and fall.
58
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44
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Figure 21.
P-04 watershed: hydrograph for the August 7,
1973, storm
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Figure 22.
P-04 watershed:
1973, storm
hydrograph for the September 9,
59
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cr
7290
4860
2430
Figure 23.
2745 ,-
2236
C/3
cr
H
cr
D
cc
SIMULATED (130,700 LITERS)
MEASURED (132,777 LITERS)
77
154 231
ELAPSED TIME (MIN)
308
385
P-04 watershed: hydrograph for the September
14, 1973, storm
SIMULATED (52,000 LITERS)
MEASURED(11,010 LITERS)
_L
162 243
ELAPSED TIME (MlN)
324
405
Figure 24
P-04 watershed: hydrograph for the December 5,
1973, storm
60
-------
14520
~ 12100
cc
IK
I-
LU
H
CC
LL
9680
7260
4840
2420-
SIMULATED (150,000 LITERS)
MEASURED (422,236 LITERS)
22
44 66
ELAPSED TIME (MINI
110
132
154
Figure 25.
P-04 watershed:
31, 1973, storm
hydrograph for the December
Total runoff for the period May 23, 1973, through the storm
of December 31, 1973, was approximately 2,400,000 liters. Simu-
lated runoff was approximately 1,900,000 liters. Thus simulated
runoff is 79% of actual on P-04, using SERL loam and 155% of
actual on P-01 using Clay parameters. By comparison the SERL
loam parameters on P-01 produce 1,419,231 liters of runoff or 65i
of actual.
Examination of the summary runoff figures shown in Tables
6 and 7 in the last part of this section does not reveal any
clear trend. Simulated results tend to be low the first couple
of months for both P-01 and P-04. Thereafter the simulated
results are consistently high on P-01 and somewhat the same
trend is seen on P-04. Simulated runoff on both P-01 and P-04
61
-------
during December is in poor agreement with measured runoff.
SERL loam on P-01 produced consistently low runoff except
for the December 5, 1973, storm which was twice measured.
There are a number of possible explanations for the disa-
greement between simulated and measured runoff:
• Poor quality control on measured data
• Rain interception on crop canopy
• Evapotranspiration model is not working properly
0 Improper specification of boundary conditions or
depth increment within model
9 Stochastic changes in the watershed - tillage,
crusting, harvest - which are not simulated
« Nonuniform rainfall over the watershed
9 Improper choice of soil type and/or improper
specification of uniform soil type throughout the
watershed.
Isolation and correction of the critical problems is a
complex process which will require additional simulation, collec'
tion of data not presently available, and additional models for
the simulation.
62
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SEDIMENT
The Foster-Meyer (F/M) sediment model, which is described
in detail in Section VII of this report, requires that the user
specify three parameters denoted K,, K_, and K_. K, is the
transport capacity parameters, K,., is the detachment capacity para-
meter, and K3 is the rainfall detachment parameter. Because the
F/M model has not been used extensively, K, , K-, and K., were set
to give reasonably good results on the first storm. In general,
it might not be a good idea to set the parameters for the first
storm because of the unusual soil conditions that may exist at
that time. However, most of the sediment and pesticide loss
occurred during the first storm on P-01 and failure to set up the
parameters properly would produce poor results.
Several problems developed during the initial tests of the
F/M sediment model within SCRAM:
1. The structure of the watersheds, which were
designed to enable the total runoff and sediment
loss to be measured, was basically incompatible
with the F/M model.
2. The F/M model does not allow for the effect of
crop canopy on the kinetic energy of rainfall
striking the ground.
3. The F/M model does not allow for the stabilization
of the soil after plowing, planting, rainfall and
of crop growth.
63
-------
The first problem is largely unavoidable. The F/M model
was designed for small rectangular plots with runoff along the
lower edge of the plot, while the experimental watersheds are
designed to empty through a flume. As a result, water and sedi-
ment are discharged into the flume from the upper portions of the
watershed. Water backs up behind the flume and the natural flow
off the watershed is lost. In addition, the total sediment which
is dumped onto the flume approach exceeds the capacity of the
flow and large amounts of sediment must be deposited.
Several modifications were made to the F/M model to account
for the above problems. In making the changes the basic struc-
ture of the model was maintained, since many users may want to
simulate watersheds without flumes.
A linear function was added to allow for crop canopy, which
causes the value of K., to decrease from plant date to harvest.
An exponent was then added which decreases the value of K, from
plant date through six months, after which K, is constant.
Finally, a limiting term (L) was added; L controls the ratio of
the sediment load at the upper end of a subplot to the sediment
load capacity of that plot at the lower end.
The limiting term L is necessary because the sediment trans-
ferred to the flume subplot may exceed the capacity of that sub-
plot by orders of magnitude. When this occurs the F/M model will
cause deposition, but on the flume subplot the rate of deposition
may be too small to reduce the sediment load at the output to
realistic levels.
P-01 Sediment Loss
In order for the sediment model to produce good results
it is necessary to accurately simulate the watershed runoff.
The F/M sediment model is not linearly dependent on the runoff
64
-------
volume and hence it is only possible to evaluate the sediment
model for those storms which have simulated hydrographs nearly
identical to the measured hydrographs.
The sediment loss for the eight major storms on P-01 between
June 13, 1973, and December 31, 1973, are shown in Figures 26
through 33. These curves correspond to the hydrographs using
clay soil parameters presented in the previous section.
One characteristic of the simulated sediment loss that is
absent in the observed curves is the large increase in sediment
concentration during the tail of the hydrograph. This result is
not unexpected. After it stops raining the water which is backed
up behind the flume is infiltrated rather rapidly. As a result
the volume of water drops and the simulated concentration of
sediment increases faster than the rate of deposition. This
error is not particularly significant since the total volume of
water remaining is generally small in comparison to the total
volume of runoff. A similar effect can sometimes be seen as
runoff begins.
Given the overprediction of runoff volume for most of the
storms using clay parameters, the sediment model is working
reasonably well. Simulated sediment loss for the June 13, 1973,
storm was 14,456 kilograms versus a measured loss of 16,388
kilograms. Since the simulated runoff was below measured runoff,
this is the expected result. Most of the other storms produce
results which appear reasonable considering the form of the
corresponding hydrograph. There are two storms which did not
produce reasonable results; they occurred on July 30, 1973, and
September 9, 1973.
The simulated sediment loss on July 30, 1973, was 21,468
kilograms (for 457,400 liters) whereas the measured loss was
only 3975 kilograms (for 354,674 liters). Much of the difference
is due to the 100,000 liters of excess simulated runoff over a
65
-------
cc
LJJ
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DC
P
0
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240 _
192 _
144
96 _
48
SIMULATED (14,456kg)
MEASURED (16,388 kg)
22
44 66
ELAPSED TIME (MINI)
110
Figure 26.
_ 180
DC
LU
P-01 watershed: sediment loss for the
June 13, 1973, storm
225 I"
~ 135
LL
o
^
X
p
2 90
LU
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45
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Figure 27
SIMULATED (7,257 kg)
MEASURED (2,367 kg)
t
/
/
/
/
/
/
I
/
V
15
30 45
ELAPSED TIME (MIN)
\
60
75
P-01 watershed: sediment loss for the
June 21, 1973, storm
66
-------
115 r-
cc
LU
I-
o
z
D
DC
H
Z
LU
Q
LU
in
DC
LU
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cc
p
Q
LU
V)
69
46
23
SIMULATED (284 kg)
MEASURED (1,361 kg)
11
22 33
ELAPSED TIME (MINI
44
55
Figure 28.
715
572
429
286
143
P-01 watershed: sediment loss for the
July 8, 1973, storm
SIMULATED (21,468 kg)
MEASURED(3,925 kg)
,A
\
\
\
\
\
15
.-/
30 45
ELAPSED TIME (MIN)
1
60
1
75
Figure 29.
P-01 watershed: sediment loss for the
July 30, 1973, storm
67
-------
DC
LU
D
CC
P
Q
LJJ
CO
DC
UJ
o
2
CC
Q
UJ
c/)
195 ,-
156
117
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39
SIMULATED (15,060 kg)
MEASURED (2,078 kg)
\
\
•—i
-r
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23
46 69
ELAPSED TIME (MIN)
92
115
Figure 30
30 _
P-01 watershed: sediment loss for the
September 9, 1973, storm
SIMULATED (3,493 kg)
MEASURED (958 kg)
15
30 45
ELAPSED TIME (MIN)
60
75
Figure 31.
P-01 watershed: sediment loss for the
September 13, 1973, storm
68
-------
Lt
LU
I-
Z
LU
Q
LU
90 _
72
54
36
18
— — — SIMULATED (2,939 kg)
MEASURED02.3 kg)
DC
LU
I-
o
z
44
Figure 32,
88 132
ELAPSED TIME (MIN)
176
220
P-01 watershed: sediment loss for the
December 5, 1973, storm
SIMULATED (4,002 kg)
MEASURED (2,285 kg)
96
192 288
ELAPSED TIME (MIN)
384
480
Figure 33
P-01 watershed: sediment loss for the
December 31, 1973, storm
69
-------
very short period. However, even allowing for this, the differ-
ence seems too large. The July 30, 1973, storm produced 2.79 cm
of rain in 30 minutes. For comparison the June 13, 1973, storm
produced 1.9 cm in 27 minutes. Total runoff was nearly the same
for both storms but the July 30, 1973, runoff lasted for some
30 minutes while the June 13, 1973, runoff continued for almost
60 minutes. In addition, the July 30, 1973, hydrograph exhibits
the novel "flat" top during the peak flow. Even allowing for
stabilization of the watershed and crop canopy, the dramatic
drop from 16,388 kilograms on June 13, 1973, to 3,925 kilograms
on July 30, 1973, is a surprise.
The significance of runoff volume on sediment loss in the
Foster-Meyer model was assessed by running the P-01 storm
sequence with SERL loam hydrologic parameters. Simulated runoff
was 65% of measured but the simulated sediment loss was 53%
of measured. For the July 30, 1973, storm, simulated runoff
dropped to 286,663 liters (81%) and the sediment loss dropped
to 4,456 kilograms (114%). The change in simulated sediment loss
from 21,468 kilograms to 4,456 kilograms indicates the sediment
model may be working reasonably well. A similar result was
observed for the September 9, 1973 storm where simulated runoff
dropped to 368,933 liters (92%) and sediment loss dropped to
2,380 kilograms (115%).
These results demonstrate that the sediment model is highly
sensitive to runoff volume. Adjustment of the sediment parame-
ters can only be made after the runoff model is functioning
properly. If the water model parameters are artificially
adjusted to produce good results for total runoff, the sediment
model will produce good results for total sediment loss. How-
ever, runoff and sediment loss for the first storm on P-01 would
be grossly under-predicted under these conditions. Since almost
70
-------
all of the diphenamid loss occurred during the first storm it
would not be possible to predict the seasonal loss of diphenamid.
P-04 Sediment Loss
The P-01 sediment parameters were not changed during the
simulation of the P-04 storms from May through December 1973.
Figures 34 through 39 illustrate the simulation results for the
major storms. Without exception the simulated loss is below the
measured loss. Although the runoff was generally low the
simulated sediment loss is down by a factor of ten or more.
The only other explanation for the dramatic difference between
P-01 and P-04 is the difference in watershed geometries. P-01
is an unterraced watershed of 2.7 hectares with an average
slope of 4% whereas P-04 is terraced, 1.25 hectares with an
average slope of 2% toward the drainage channels. The difference
in runoff volume can account for a factor of five as was seen
by the results for P-01 using SERL loam. The remaining
difference is due to the nonlinear dependence of the sediment
model on slope.
PESTICIDE LOSS VIA RUNOFF AND EROSION
The simulation of pesticide loss in the runoff and on the
sediment is dependent on accurately predicting runoff, sediment
loss, the proper adsorption-desorption rates, and degradation
rates for the entire growing season. It is especially critical
for the storms immediately following the pesticide application
when pesticide loss is highest. Thus, evaluation of pesticide
loss predictions can only be performed by properly considering
the total system involved.
71
-------
D
JT
Q
LU
C/D
140 r
112
84
56
28
/I
14 21
ELAPSED TIME (MINI
' — SIMULATED (2.5 kg)
— MEASUREDI13.5 kg)
28
35
Figure 34,
(D
u_
LL
O
H
Z
60 r
48
36
24
12
II
II
Ml
I I
I I
P-04 watershed: sediment loss for the
May 23, 1973, storm
— SIMULATED (107 kg)
MEASURED (1,603 kg)
100
125
Figure 35.
ELAPSED TIME (MIN)
P-04 watershed: sediment loss for the
May 28, 1973, storm
72
-------
190
£ 152
UJ
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P
IE 76
5
LU
CO
38
0
1
1
1
1
L j
Jf . ' 7 T—
77
154 231
ELAPSED TIME (MINI
SIMULATED (48 kg)
MEASURED (1,613 kg)
308
385
Figure 36
150
£ 120
LU
H
e>
t 90
SEDIMENT/RUNO
O)
0
30
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.i
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Figure 37,
P-04 watershed: sediment loss for the
May 28, 1973, storm
— SIMULATED (72 kg)
——^ MEASURED(796 kg)
17
34 51
ELAPSED TIME (MIN)
P-04 watershed: sediment loss for the
June 6, 1973, storm
73
-------
110 _
cr
LU
Z
LU
S
to
SIMULATED (78 kg)
MEASURED (756 kg)
58 87
ELAPSED TIME (MIN)
116
145
Figure 38,
1b
LU 12
H
DIMENT/RUNOFF (GM
°>
LU
CO
3
-
|
r/ I
.1 1
I 1
\J 1
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Figure 39,
P-04 watershed: sediment loss for the
July 8, 1973, storm
• SIMULATED (6 kg)
MEASURED (89 kg)
44 66
ELAPSED TIME (MIN)
88
110
P-04 watershed: sediment loss for the
September 9, 1973, storm
74
-------
At the present time a deterministic model to describe
the mass transfer of pesticide from the zone of erodibility,
i.e., across the boundary separating the moving runoff film
and soil surface has been conceptualized but not developed.
Four mechanisms are potentially involved: (1) diffusion
plus turbulent transport of dissolved pesticide from the soil
interstices, (2) pesticide desorption from sediment particles,
(3) dissolution of crystalline pesticide at the boundary, and
(4) dissolution of crystalline pesticide carried with the
sediment.
In the absence of an available deterministic model, a sim-
ple empirical approach has been utilized in SCRAM. Turbulent
transport is assumed to be related to the depth of runoff on a
subplot. Due to the formation of rills and the soil surface dy-
namics, runoff is assumed to interact with the dissolved
pesticide in the soil intertices to a depth of two centimeters.
The surface area of runoff interactions is assumed to decrease
exponential from plant date to harvest.
Mathematically, the pesticide mass transfer to the runoff
water is expressed as:
Loss (H20) = [RO • 2.2 • 10~4 • C - e~(MO)] (1)
where Loss (H~O) = grams loss in the runoff
RO = runoff volume (£)
2.2 • 10~ = proportionality factor
C = average micrograms of pesticide in solu-
tion in the top two layers
e = factor accounting for the crusting and
formation of rills thereby reducing
surface area affected by runoff
MO = months since plant date
75
-------
Pesticide Loss in the Runoff
Diphenamid was applied on P-01 on June 13, 1973. Figure
40 shows the simulated rate of pesticide loss in the runoff for
June 13, 1973, storm compared to the measured values. A loss
of 608 grams was measured. The general shape of the graphs
indicates that the predicted rate of diphenamid loss does not
significantly deviate from the loss actually observed.
Figures 41 and 42 show similar graphs of diphenamid loss
in the runoffs on June 21, 1973, and July 8, 1973, with measured
losses of 27-6 grams and 1.77 grams, respectively. The model
overpredicts in the amount of diphenamid loss in the runoff on
June 21, 1973, (133 grams) and on July 8, 1973, (4.16 grams).
On July 21, 1973, however, WATER overpredicts the amount
of runoff and DEGRAD leaves more diphenamid in the soil profile
than was measured, causing the high loss predicted. On July 8,
1973, WATER underpredicts the volume of runoff but DEGRAD still
leaves more diphenamid in the soil profile than was measured.
Hence, SCRAM still overpredicts the diphenamid loss in the runoff
but not by as large a margin. Simulated and measured losses of
diphenamid during the period from July 8, 1973, through September
9, 1973 were not significant.
Figures 43 through 45 show the atrazine loss in the runoff
for the May 28, 1973 (AM), May 28, 1973 (PM), and June 6, 1973,
storms. SCRAM predicted losses of 87, 44, and 9 grams respec-
tively, whereas measured losses were 17, 14, and 3 grams.
Most of the difference in the totals can be attributed
to the degradation model. On May 28, 1973, approximately 90
percent of the atrazine was degraded, whereas simulated degrada-
tion was 53 percent. Similarly, by June 6, 1973, 93% was degrad-
ed, whereas simulated degradation was 75%.
76
-------
60
40
C/3
in
O
I
Q_
5
20
.- SIMULATED
— MEASURED
12 18
ELAPSED TIME MIN
24
30
Figure 40.
P-01 watershed: rate of diphenamid loss in
runoff for the June 13, 1973, storm
8.0
SIMULATED
MEASURED
to
to
O
Z
01
I
4.0
2.0
10
20 30
ELAPSED TIME MIN
40
50
Figure 41.
P-01 watershed: rate of diphenamid loss in
runoff for the June 21, 1973, storm
77
-------
0.20 I—
0.15 -
SIMULATED
MEASURED
J 0.10 _
z
UJ
I
Q-
a 0.05 -
Figure 42,
ELAPSED TIME (MINI
P-01 watershed: rate of diphenamid loss in
runoff for the July 8, 1973, storm
3.0|—
- 2.0
CO
(f>
O
N
DC
I-
<
1.0
SIMULATED
MEASURED
40 60
ELAPSED TIME (MINI
80
100
Figure 43.
P-04 watershed: rate of atrazine loss in
runoff for the May 28, 1973, storm (AM)
78
-------
3.0
- 2.0
01
Z
DC
1.0
100
SIMULATED
MEASURED
200 300
ELAPSED TIME (MINI
400
500
Figure 44,
P-04 watershed: rate of atrazine loss in
runoff for the May 28, 1973, storm (PM)
0.75,
SIMULATED
MEASURED
10
20 30
ELAPSED TIME (MINI
40
Figure 45.
P-04 watershed: rate of atrazine loss in
runoff for the June 6, 1973, storm
79
-------
The difference between the shape of the curves is unexpect-
ed. Simulated atrazine losses are proportional to the runoff
depth on each subplot and hence the rate of loss increases during
peak runoff. The measured rate of atrazine loss is relatively
flat and does not show any significant response to peak runoff
flows. Since P-01 pesticide loss does show a response to runoff
rate, the change is probably related to the watershed topography,
crop type, and conservation practices. P-01 was planted in soy-
beans, was not terraced, and had an average slope twice that of
P-04, which was terraced and planted in corn. Runoff from P-04
will tend to interact with the soil surface to a lesser degree
than runoff does on P-01. Once runoff flow begins on P-04 the
interaction with the soil may not change significantly even
though the average runoff depth increases. This would produce a
constant rate of atrazine loss.
Measured losses of atrazine in the runoff were insignificant
after June 6, 1973, because degradation was nearly complete.
Simulated losses were not significant because of degradation and
the simulated movement of atrazine into the soil profile which
rapidly depleted atrazine concentrations in the top soil levels.
Pesticide Loss on the Sediment
The amount of pesticide transported on the sediment will
depend on: (1) the origin of the sediment due to areal variation
in pesticide application (2) desorption of pesticide from the
sediment during runoff, (3) adsorption due to dissolution of
crystalline pesticide, and (4) the depth of the interaction zone
between runoff water and the soil profile.
80
-------
In the absence of a developed deterministic model an empi-
rical model is presently included in SCRAM. For each subplot
the concentration of pesticide on the sediment is assumed to be
proportional to the sediment load, the concentration of absorb-
ed pesticide in the upper two centimeters, and the elapsed time
since plant date. Mathematically:
Loss (SED) = [SED • 0.08 • S • e (MO)]
(2;
where Loss (SED) = grams of pesticide loss on sediment
SED = grams of sediment loss
0.08 = proportionality factor
S = average micrograms of adsorbed pesticide
in the top two layers
e = factor accounting for the crusting and
formation of rills thereby reducing the
surface interaction area
MO = months since plant date.
Figures 46 and 47 show the simulated and measured diphenamid
sediment concentrations on P-01 for the June 13, 1973, and June
21, 1973, storms. Although the simulated curves do not have the
same shape as the measured curves, the total simulated losses
(8.8 and 2.8 grams) compare favorably with the measured losses
of 10.5 and 1.6 grams. Simulated and measured losses on the
sediment were not significant after June 21, 1973 (<7%).
Simulated losses of atrazine on the sediment exhibit the
same behavior as diphenamid on P-01. However, the simulated
sediment loss is less than 10% of the measured loss, hence
simulated atrazine loss on the sediment is not significant.
Accordingly, the corresponding graphs are not shown.
81
-------
1.5
cr
H
Z
o
o
LU
I
D_
1.0
0.5
SIMULATED
MEASURED
1
5
z
o
LU
u
z
o
o
Q
25
50 75
ELAPSED TIME (MIN)
100
125
Figure 46,
1.0
.75
P-01 watershed: diphenamid loss on the
sediment (yg/g) for the June 13, 1973,
storm
SIMULATED
MEASURED
z
111
I
Q_
.25
Figure 47.
25
_L
I
50 75
ELAPSED TIME (MIN)
100
125
P-01 watershed: diphenamid loss on the
sediment (yq/g) for the June 21, 1973,
storm
82
-------
Simulated pesticide concentration on the sediment is con-
stant for several reasons: (1) the exponential factor in the
model does not change within a runoff event, (2) the average
concentration of adsorbed pesticide in the upper two centimeters
does not change significantly during the runoff period, (3) the
application of pesticide was assumed to be a constant over the
entire watershed, and (4) the present model averages the concen-
trations from each subplot at the confluence of the watershed.
Significant changes in the model and simulation structure will be
required to eliminate this effect.
PESTICIDE MOVEMENT IN THE SOIL PROFILE
The pesticide movement model (ADDE) (described in detail in
Section VII of this report) simulates the movement of pesticides
into the soil and the dispersal of the pesticide in the soil
profile. The pesticides modeled in the simulation were diphena-
mid and atrazine, which were applied, respectively, to water-
sheds P-01 and P-04. Both of the pesticides are water soluble
and were applied as a wettable powder at a rate of 3.36 kg/ha.
The adsorption-desorption model requires four input para-
meters: AB and N, the exponential coefficients; K, the adsorp-
tion coefficient; and D, the diffusion coefficient. The
adsorption-desorption model is also sensitive to the thickness
of the soil layer, which is a user supplied parameter determined
by the requirements of other submodels. The adsorption coeffi-
cient, K, was the only parameter assigned different values (see
Table 4) for diphenamid and atrazine. The movement of pesticides
into the soil profile interacts with several other processes
involved in the simulation of pesticide transport on a watershed.
The degradation model, DEGRAD, determines the remaining level of
pesticide, which is available for movement by the adsorption-
desorption model. The infiltration model, WATER, and
83
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evapotranspiration model, EVAP, provide the water movement
parameters which effect the rates of adsorption-desorption and
pesticide dispersion. In order to evaluate the results of ADDE,
while minimizing the effects of DEGRAD, pesticide concentrations
are discussed as the percentage per soil level of the total
pesticide concentration remaining in the soil. The dependence
upon infiltration velocities calculated by WATER cannot be
eliminated in the analysis of the ADDE submodel.
To compare the simulated results of ADDE to the core sample
data, the SCRAM results were adjusted from the 1 cm soil layers,
predicted by the model, to the experimental core sample intervals
(Figure 48). Model predictions were made to a soil depth of
15 cm, which corresponds to the first five core sample intervals
(0-1.0 cm, 1-2.5 cm, 2.5-5.0 cm, 5.0-7.5 cm, and 7.5-15.0 cm).
Experimental sample levels between 15-22.5 cm and 22.5-30 cm are
not shown because significant movement did not occur below 15
cm. The procedure used to convert pesticide concentration from
ppb to percent is shown in Table 5.
Both the measured and simulated data points were plotted
as bars and then a smooth curve drawn to reduce the distortion
caused by the sampling levels. The bars are not shown on the
graphs because they obscure the difference between the simulated
and measured profiles.
Diphenamid Movement and Dispersion on P-01
The first storm after diphenamid application occurred
on the same day, June 13, 1973. Significant amounts (6%) of
diphenamid were found below five centimeters, whereas the model
predicts all of the pesticide should be above five centimeters
(Figure 49).
84
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TABLE 4.
ADDE PARAMETERS USED IN THE SCRAM SIMULATION
OF PESTICIDE MOVEMENT ON WATERSHEDS P-01
AND P-04
Watershed Pesticide Parameter Description Parameter Parameter Value
P-01 Diphenamid Exponential Coefficient
P-01 Diphenamid Exponential Coefficient
P-01 Diphenamid Adsorption Coefficient
P-01 Diphenamid Diffusion Coefficient
P-04 Atrazine Exponential Coefficient
P-04 Atrazine Exponential Coefficient
P-04 Atrazine Adsorption Coefficient
P-04 Atrazine Diffusion Coefficient
AB
N
K
D
AB
N
K
D
1.7
0.9
1.5
0.1
1.7
0.9
1.0
0.1
TABLE 5.
PROCEDURE FOR CALCULATING THE PERCENT PESTICIDE
PER SAMPLE LEVEL
Level
#
1
2
3
4
5
Depth Concentration Mass* Percent
cm ppb ng %
0-1 26,000 39,000 78
1-2.5 1,455 3,274 6
2.5-5.0 1,322 4,958 10
5.0-7.5 500 1,875 4
7.5-15.0 107 1,205 2
* Soil bulk density = 1.5 g/cm"
85
-------
SOIL SURFACE
O
t/i
O
2
I
I-
0.
UJ
O
150
10.0
Figure 48
Q.
UJ
Q
Diagram of core samples used in analysis of
experimental data
20
% DIPHENAMID
40 60
100
I
SIMULATED
MEASURED
EXPERIMENTAL SAMPLING INTERVALS
Figure 49
P-01 watershed: simulated and measured
distribution in the soil profile on
June 13, 1973
86
-------
There is no obvious explanation for the difference. The
storm produced 1.9 cm of rain over a 26 minute period and 1.37 cm
was runoff, leaving 0.53 cm to infiltrate into the soil profile.
This is not enough water to carry pesticide below 10 centimeters.
The residual diphenamid levels on P-01 measured on June 12, 1973,
are insignificant in comparison to the levels measured on June
13, 1973. Interestingly, the same type of distribution was
measured on P-03 on June 15, 1973, even though no rainfall was
recorded on that date (which was also the application date).
Hence, sample contamination seems probable, especially since the
surface concentration is fifty times the concentration below
5 cm.
A total of 5.0 cm of rain, most of which was infiltrated,
fell between June 13, 1973, and the next sample date, which was
July 9, 1973. The simulated distribution has started to move
into the soil profile, whereas the measured distribution still
shows the highest percentage at the soil surface (Figure 50).
By this time more than 90 percent of the diphenamid has been
degraded in levels one, two, and three (0-5 cm), while the
concentrations in levels four and five have returned to the
residual preplant concentrations. Hence, the portion of the
curves below five centimeters is of little significance, even
though this is a significant percentage of the total remaining
pesticide.
The next experimental core samples were taken on August 1,
1973. More than five centimeters of rain fell in the interim.
Dispersion has increased in the simulated pesticide distribution
and the peak is close to five centimeters. The measured distri-
bution retains the characteristic higher concentration at the
surface (Figure 51). The same type of distribution was observed
on the P-03 watershed.
87
-------
DIPHENAMID
I
Q_
HI
Q
20
40
60
80
10
15 "—
100
—1
Figure 50,
SIMULATED
MEASURED
P-01 watershed: simulated and measured
distribution in the ;soil profile on
July 8, 1973
One explanation for this, which has been postulated by
SERL staff, is that some of the diphenamid may be permanently
attached to the soil particles. Although permanent attachment
would only occur for a small percentage of the pesticide, as
the season progressed the concentration at the surface would not
be depleted by infiltration.
The final core samples were taken on September 12, 1973.
By this time most of the diphenamid has degraded. There is very
little difference between the measured concentrations below one
centimeter and the residual concentrations measured before
application. The measured concentration in the first centimeter
is slightly higher than the preapplication residual, but is of
doubtful significance due to the effects of soil erosion and
sediment deposition. Simulated concentrations are zero in the
top few centimeters due to the effects of degradation and the
amount of water that has infiltrated into the soil.
-------
Atrazine Movement and Dispersion on P-04.
Atrazine was surface applied to P-04 on May 11, 1973. A
total of 13.98 centimeters of rain fell between the application
date and May 30, 1973. Approximately 8.4 centimeters of the
rain was infiltrated. The measured atrazine profile is dispers-
ed wider and deeper in the soil profile than in the simulated
profile (Figure 52). Since simulated runoff was below measured
runoff for the same period, the difference is not due to the
WATER model.
Between May 30, 1973, and June 8, 1973, an additional 9
centimeters of rain fell, of which approximately 6 centimeters
was infiltrated. The simulated atrazine distribution is reason-
ably close to the measured profile (Figure 53). There are two
differences: (1) the simulated atrazine concentrations are close
to zero and below measured concentrations at the surface, and
(2) the simulated atrazine concentrations below 8 centimeters
are less than measured levels. The difference at the surface
is probably partially due to the effects of sediment movement
and deposition and sampling difficulties. Atrazine movement
in significant amounts below 8 centimeters is not expected for
the present model and specified parameters.
On July 10, 1973, the final set of core samples were taken
on P-04. Ten centimeters of rain fell in the interim (6.4
centimeters on July 8, 1973) and approximately 7 centimeters
was infiltrated. The simulated and measured distributions
are markedly different (Figure 54). Very little atrazine
remains on the watershed at this time (< 3 percent), hence the
difference is not particularly significant. Nevertheless, the
characteristic presence of measureable levels of atrazine at
the surface and below ten centimeters is evident.
89
-------
I
Q.
DIPHENAMID
40
60
10
80
T
100
1
15 I—
SIMULATED
MEASURED
Figure 51.
P-01 watershed: simulated and measured
distribution in the soil profile on
August 1, 1973
i ATRAZINE
I
Q.
Ill
Q
o
to
10
15
60
80
100
_ SIMULATED
MEASURED
Figure 52,
P-04 watershed: simulated and measured
distribution in the soil profile on
May 23, 1973
90
-------
% ATRAZINE
40 60
80
100
Q_
LJJ
Q
O
VI
10
SIMULATED
______ MEASURED
15
Figure 53.
P-04 watershed: atrazine soil profile
distribution on June 8, 1973
O.
111
Q
20
% ATRAZINE
40 60
80
10
15
SIMULATED
MEASURED
100
Figure 54,
P-04 watershed: atrazine soil profile
distribution on July 10, 1973
91
-------
As noted above, the interdependences between the simulation
submodels makes it difficult to assess the adequacy of the
adsorption-desorption submodel. However, a few observations
are in order. The presence of pesticide is suspect below ten
centimeters immediately after application and before significant
rainfall has occurred. Sample contamination seems likely.
The persistence of pesticide in the upper few centimeters of
soil throughout the season is unexpected. This could be
explained if some of the pesticide is adsorbed permanently -
The permanently adsorbed pesticide would not be moved into the
soil profile and could be less susceptible to degradation
processes. Finally, significant distortion of the pesticide
profile may result from the sampling intervals used in the
measurement program. The effect is partially compensated by
distorting the simulated data in the same fashion. However,
considering the rate of degradation and the experimental problems
involved, sampling intervals of 0-2 cm, 2-4 cm, 4-6 cm, 6-8 cm,
8-10 cm, and 10-20 cm are preferable.
DEGRADATION
The simulation of the diphenamid degradation on the P-01
watershed utilizes two simplifying assumptions. The first
assumption is uniform application of the herbicide over the
watershed. The figure used in the simulation as the application
2
rate was 33.66 yg/cm , based on a uniform application of 3.36
kg/ha.
The second assumption, which may have significantly
influenced the simulation results, involves the soil temperature.
A uniform soil temperature in the range of 25-28°C was assumed
throughout the soil profile. Temperature profile data from the
attenuation plots could not be used because of data gaps and
92
-------
inconsistencies. In addition, the number of input cards to the
simulation would be unmanageable if soil temperature profiles
were included.
As a result of assuming that soil temperature is uniform,
the degradation rate in the upper levels is below actual. During
periods when the soil is dry the degradation model is not partic-
ularly sensitive to soil temperature. During periods when the
soil is moist the temperature profile is more nearly uniform. As
the crop canopy develops the soil temperature gradient is reduced.
Finally, the adsorption-desorption model rapidly removes pesti-
cide from the soil surface and hence the uniform soil temperature
assumption will not have a significant effect on the simulation
results.
The experimental core samples were collected from each
of the ten subplots on P-01. There is a large variation among
samples from the same level but different subplots. Comparison
between simulated and measured levels on a subplot basis is also
difficult due to the effects of sediment transport and deposi-
tion. Because of this the simulated and experimental results
for each subplot and all levels were averaged to produce a
watershed degradation curve.
Diphenamid degradation for P-01, simulated and measured,
is plotted in Figure 55. Measured degradation is much more rapid
than simulated degradation. Within 30 days 95 percent of the
diphenamid has been degraded and within 60 days nearly 99 percent
has been degraded. Simulated degradation proceeds at a slower
rate but does approach 100 percent after 90 days, which is
consistent with the model.
The same model assumptions and parameters were used to simu-
late atrazine degradation on P-04 (Figure 56). Measured degrada-
tion is very rapid during the first 30 days (~ 95%) and only
trace amounts remain after 60 days. The simulated degradation
93
-------
100
80
60
2
LU
I
O.
SIMULATED
MEASURED
20
40 60
ELAPSED TIME (DAYS)
80
100
Figure 55.
P-01 watershed: degradation of diphenamid
in the soil profile after application on
June 13, 1973
100
SIMULATED
MEASURED
2
<
LU
tt
2
N
a:
20 _
40 60
ELAPSED TIME (DAYS)
80
100
Figure 56.
P-04 watershed: degradation of atrazine in
the soil profile after application on
May 11, 1973
94
-------
curve lags the measured curve but does approach the axis asym-
ptotically as required by the model. The simulated degradation
curve is offset from the vertical axis because the pesticide is
not introduced into the simulation until the first rain occurs
(May 19, 1973), whereas the application date was May 11, 1973.
Simulated degradation depends on the soil moisture profile,
the soil temperature profile, and the pesticide distribution in
the soil profile. The infiltration model and the evapotranspira-
tion model determine the soil moisture profile. At the present
time SCRAM does not contain a soil temperature model. The
adsorption-desorption model results suggest that pesticide is
moved into the soil profile too rapidly. The combined effect of
these three models on the degradation results is difficult to
determine because in this model parameters were not adjusted
from specified values to improve the results. Also, based upon
the sensitivity analyses (Section VII), even if the degradation
parameters are set for maximum degradation the simulated rate
of degradation would be below the measured rate. Because of this
the degradation model may require further development to improve
the simulated results.
VOLATILIZATION
Trifluralin (a, a, a-trifluoro - 2, 6-dinitro-N,
N-dipropyl-p-toluidine) was selected to test the volatilization
submodel included in SCRAM. Trifluralin was applied to both the
P-01 and P-03 watersheds. Data was available from the date of
application on the total amounts of trifluralin still on the
watershed and the amount of trifluralin distributed in the soil
profile.
The application rate on both watersheds was specified
as 1.12 kg/ha (incorporated). However, immediately after appli-
cation the average of the core samples indicated that a large
95
-------
amount of trifluralin had already been lost (38 to 56%) due to
volatilization or experimental error.
Uncertainty in the application amount and/or rapid
volatilization also creates uncertainty as to what the initial
pesticide distribution in the soil profile should be. Figure 57
is a graph of the initial pesticide profile at the time of the
first observation during 1973 on P-01 and P-03. Also shown in
Figure 57 is a starting profile distribution that was frequently
used during the simulation.
The simulation was started with a trifluralin profile
which was higher than measured in the upper layers and below
measured concentration in the lower layers. This is intended to
allow for losses and redistribution before the first samples
were taken. The simulated application amount was taken as 5700
2
ng/cm unless noted otherwise.
Initially, the diffusion coefficient for trifluralin at
each depth increment was calculated from the equation developed
by Bode for Mexico Silt Loam (2.5% organic matter, 75% silt,
22% clay and a pH of 5.6). This was not successful because at
the present time SCRAM does not contain a model to predict the
soil temperature profile and at a bulk density of 1.6 g/cc the
Bode equation generates diffusion coefficients which are less
-7 2
than 3x10 cm /sec if the soil temperature is below 40°C.
Diffusion coefficients for trifluralin in Lanton Silty
— 72 21
Clay Loam between 0.2 and 0.5 x 10 cm /sec have been reported.
Diffusion coefficients less than 10~ cm /sec do not cause
significant losses of trifluralin with the present model.
One explanation for the unusually large diffusion coeffi-
cients required in the model would be the effect of significant
degradation. However, there is no positive evidence of photo-
O O
decomposition on soils and microbial degradation is minimal.
96
-------
2500 r
SIMULATED
FIRST OBSERVATION P-03
_ FIRST OBSERVATION P-01
P-03 (15 JUNE 73)
10
12
14
16
Figure 57.
Distribution of trifluralin in the soil profile
Because of this it was necessary to treat the diffusion coeffi-
cient as a constant independent of soil temperature and soil
moisture content. As a result the diffusion coefficient becomes
a simulation parameter.
There is very little difference between the P-01 and P-03
trifluralin losses as a function of time. Figures 58 and 59
show the percent of trifluralin remaining since the application
date for the P-01 watershed. The solid curves represent the
smoothed data for two different application rates. Curves
labeled "I" represent an application rate derived by adding 10%
to the amount found at the time of the first sampling. Curves
2
labeled "II" represent the amount remaining if 11,220 ng/cm was
applied.
97
-------
Since the diffusion coefficient must be treated as a
parameter independent of soil moisture, the only difference
between P-01 and P-03 is the application amount and the initial
distribution in the soil profile. Based on Figure 57 there may
have been different initial distributions. However, the different
rainfall records observed after application could also account for
the different profile distributions. Because of these uncer-
tainties the loss of trifluralin has been simulated for several
initial distributions and several values of the diffusion
coefficient. The results were then compared to both P-01 and
P-03 experimental data.
The first trifluralin distribution tested was similar to
that observed on P-03 on a percent per centimeter basis.
Represented as a vector basis, the distribution is as follows:
45.8, 27.5, 14.2, 6.4, 3.3, 1.8, 0.8, 0.2. The diffusion coef-
-7 2 -2 2
ficient was set at 8x10 cm /sec (6.9x10 cm /day). As shown
in Figures 58 and 59 (curves labeled "A"), the simulated triflur-
alin loss follows the observed loss closely for the first 25 to
30 days and then falls behind when compared to an application
rate ("I") near that observed on the day of application. If the
assumed application rate is near the specified rate, the
diffusion coefficient must be increased by a factor of 100 to
produce results which compare to those measured. See Figure 58
and 59 curves "II" and "C".
Regardless of how the initial profile is specified or how
large the diffusion coefficient is, the present model does not
adequately predict the loss of trifluralin. Observed losses drop
off rapidly during the first 20 days or so and then seem to drop
in a linear fashion during the remaining 70-80 days. None of
the available models will predict this behavior.
98
-------
o
z
LU
cc
100
80
60
40
20
____ SIMULATED MODEL II (MOD 2)
A = D AT 6.9 x 10~2 cm2/DAY
B = D AT 8.6 x 10"2 cm2/DAY
C = D AT 8.6 x 10~1 cm2/DAY
I = APPLICATION OF 4745 ng/cm
= APPLICATION OF 11,220 ng/cm
40 60
DAYS SINCE APPLICATION
100
O
Z
UJ
DC
DC
D
100
60
40
20
Figure 58,
P-01 watershed: trifluralin
remaining after application date
SIMULATED MODEL 11 (MOD 2)
MEASURED
6905 ng/cm2
11220 ng/cm2
20
40
60
80
100
DAYS SINCE APPLICATION
Figure 59.
P-03 watershed: trifluralin
remaining after application data
99
-------
The volatilization model designated as Model II (Mod 2) also
predicts diffusion of pesticide in the soil profile according
to the concentration gradient. Experimental data shown in
Figures 60 and 61 illustrate the tendency for the pesticide to
approach a nearly uniform distribution in the soil profile.
Simulation results for two different values of the diffusion
coefficient are shown in Figures 62 and 63. Although the
simulation results are calculated on a per centimeter basis,
they have been graphed to correspond to the experimental
depth increments.
The volatilization model predicts pesticide movement
in the soil profile in close agreement with the experimental
results. Simulated volatilization loss does not correlate
well with the periodic measured loss, and unusually large values
for the diffusion coefficient are required to predict total
losses which approach measured losses.
SIX MONTH SUMMARY
In the previous sections the simulation results were
discussed for each major runoff event. A large number of storms
occurred between the major events which were not discussed.
Runoff, sediment, and pesticide loss for the entire period
simulated are presented below as an aid in evaluating the
simulation results.
Table 6 displays the simulated and measured results for
P-01 (2.70 ha) between June 13, 1973, and December 31, 1973. A
total of 49.6 cm of rain was recorded, producing 2,179,497 liters
of runoff (16%) and 29,999 kilograms of sediment. Measured
diphenamid loss was 652 grams or 7 percent of the total
100
-------
1500,.
cc
D
CC
H
CD
0.
1000
500
5.0-7.5 cm-
20
40 60
DAYS SINCE APPLICATION
80
100
Figure 60.
P-01 watershed: average trifluralin
concentration as a function of soil
depth - 1973
2000
1000
o:
D
en
h-
CQ
Q.
CL
500
20
40 60
DAYS AFTER APPLICATION
Figure 61,
P-03 watershed: average trifluralin
concentration as a function of soil
depth - 1973
101
-------
1500
ca
Q-
cc
z
ill
o
z
o
o
cc
D
1000
500
20
40 60
ELAPSED TIME (DAYS)
80
100
Figure 62,
1500
P-01 watershed: simulated volatilization
and diffusion of trifluralin from June to
September, 1973 (D - 10. x 10~6 cm2/sec)
CO
D-
z
o
£E 1000
z
LU
o
z
o
o
CC
D
500
-O-
20
40
ELAPSED TIME (DAYS)
60
80
Figure 63.
100
P-01 watershed: simulation
volatilization and movement
of trifluralin from June to_fi -
September, 1973 (D = 2 x 10 cm /sec)
102
-------
Table 6. P-01 WATERSHED: MEASURED VS. SIMULATED RUNOFF,
SEDIMENT AND DIPHENAMID LOSS - JUNE TO
DECEMBER, 1973
STORM DATE
AND
RAINFALL (cm)
13 JUNE 73
(1.9)
20 JUNE 73
(0.10)
21 JUNE 73
(1.9)
25 JUNE 73
(0.51)
28 JUNE 73
(0.41)
28 JUNE 73
(0.38)
8 JULY 73
(1.7)
16 JULY 73
(0.89)
17 JULY 73
(0.76)
25 JULY 73
(0.38)
30 JULY 73
(2.79)
1 AUGUST 73
(0.64)
17 AUGUST 73
(1.14)
18 AUGUST 73
(0.89)
31 AUGUST 73
(0.51)
3 SEPTEMBER 73
(0.69)
9 SEPTEMBER 73
(4.06)
13 SEPTEMBER 73
(3.18)
14 SEPTEMBER 73
(0.69)
RUNOFF* (I)
369,445
335,297
—
112,397
183,487
—
—
15,763
132,821
32,938
-
25,824
11,187
—
354,674
457,400
_
2,099
35,223
34,167
45,789
-
-
400,461
641,508
224,742
286,226
10,625
SEDIMENT* (kg)
16,388
14,456
—
2,367
7,257
-
—
259
1,361
284
—
133
99
—
3,925
21 ,468
—
13
1,922
213
4,114
-
-
2,078
1 5,060
958
3,493
45
DIPHENAMID LOSS* (g)
SEDIMENT
10.5
8.8
-
1.59
2.76
-
—
0.05
0.22
0.01
—
0.05
0.002
-
0.47
0.19
—
0.008
0.0004
0.017
-
—
0.129
—
RUNOFF
608.
556.
-
27.6
133.
-
—
1.02
1.77
4.16
-
0.26
0.05
-
0.71
1.50
—
0.02
0.003
0.034
-
-
—
—
1
TOTAL
618.5
564.8
—
29.2
176.8
-
—
1.07
1.99
4.17
—
0.31
0.052
—
1.18
1.69
—
0.03
.0034
0.05
-
-
0.13
-
1
•MEASURED
SIMULATED
103
-------
Table 6.
- Continued.
STORM DATE
AND
RAINFALL (cm)
17 SEPTEMBER 73
(0.38)
18 SEPTEMBER 73
(0.46)
27 SEPTEMBER 73
(0.76)
28 SEPTEMBER 73
(0.38)
31 SEPTEMBER 73
(1.40)
30 OCTOBER 73
(0.66)
21 NOVEMBER 73
(2.08)
25 NOVEMBER 73
(0.58)
26 NOVEMBER 73
(0.38)
28 NOVEMBER 73
(1.40)
4 DECEMBER 73
(0.20)
5 DECEMBER 73
(3.99)
15 DECEMBER 73
(1.65)
16 DECEMBER 73
(0.25)
20 DECEMBER 73
(1.93)
25 DECEMBER 73
(1.19)
26 DECEMBER 73
(0.64)
30 DECEMBER 73
(2.51)
31 DECEMBER 73
(5.26)
TOTALS
RUNOFF* (I)
_
-
-
—
7,981
-
61,956
-
-
-
_
21,360
458,169
-
-
7,362
84,076
_
_
63,404
478,382
657,600
2,179,497
3,372,866
SEDIMENT* (kg)
—
-
-
-
33
—
318
-
-
—
—
12
2,939
—
—
7
367
—
—
1,743
2,285
4,001
29,999
77,599
DIPHENAMID LOSS* (g)
SEDIMENT
-
-
—
-
-
-
-
-
-
-
—
-
-
—
-
-
-
_
-
13.
11.
RUNOFF
-
-
—
—
-
—
—
—
-
-
-
-
-
-
-
-
-
—
-
639.
695.
TOTAL
-
—
—
-
-
-
—
-
-
-
-
-
-
-
-
-
-
-
-
652.
706.
•MEASURED
SIMULATED
104
-------
application. Ninety-eight percent of the measured diphenamid
loss was in the runoff (639 grams), with only 2 percent (13
grams) on the sediment. Ninety-five percent of the loss
occurred on the application date as a result of a cloudburst
of 1.9 cm of rain which produced 72 percent runoff.
SCRAM used the 49.62 cm of rain as input and predicted
a total of 3,372,866 liters of runoff (25%) and 77,599 kilograms
of sediment using clay soil parameters. Simulated diphenamid
loss was 706 grams, 695 grams in the runoff, and 11 grams on the
sediment. Changing the soil parameters to SERL loam reduces
simulated runoff to 1,418,231 liters and sediment loss to
15,769 kilograms.
Summary results for the P-04 (1.38 ha) watershed between
May 19, 1973, and December 31, 1973, are presented in Table 7.
A total of 83.82 cm of rain was recorded on P-04, producing
measured runoff of 2,356,473 liters (20%) and measured sediment
loss of 5,525 kilograms. Total atrazine loss was 39 grams (<1%),
37 grams in the runoff and 2 grams on the sediment. The differ-
ence between P-01 and P-04, with respect to pesticide loss, is
probably due to the occurrence of heavy runoff on the application
date on P-01.
Simulated runoff on P-04 using SERL loam soil parameters
was 1,876,846 liters (16%). Simulated sediment loss was only
348 kilograms (6% of measured) using P-01 parameters. Simulated
atrazine loss was 164 grams (4%) all of which was in the runoff
because of the low sediment predictions.
The low simulated sediment losses on P-04 were unexpected.
Based upon the differences in slope and watershed size the same
rainfall on P-04 should produce approximately 25% as much sedi-
ment as on P-01. Although no exact comparisons are possible,
the difference between simulated values is much larger than 25%.
105
-------
Table 7. P-04 WATERSHED: MEASURED VS. SIMULATED
RUNOFF, SEDIMENT, AND ATRAZINE LOSS -
MAY TO DECEMBER, 1973
STORM DATE
AND
RAINFALL (cm)
19 MAY 73
(2.64)
23 MAY 73
(1.22)
24 MAY 73
(0.97)
28 MAY 73
(4.83)
28 MAY 73
(4.32)
1 JUNE 73
(0.64)
5 JUNE 73
(1.02)
6JUNE73
(3.94)
7 JUNE 73
(2.29)
7 JUNE 73
(1.12)
13 JUNE 73
(0.89)
20 JUNE 73
(0.97)
21 JUNE 73
(0.48)
28 JUNE 73
(0.61)
28 JUNE 73
(0.58)
4 JULY 73
(0.30)
8 JULY 73
(6.4)
14 JULY 73
(1.9)
16 JULY 73
(0.33)
17 JULY73
(0.94)
RUNOFF*(I)
13,361
2,609
6,365
-
356,894
263,700
337,243
187,850
-
—
280,593
241,810
—
80,515
55,040
16,772
1,970
-
—
—
200
—
411,185
464,050
61,563
49,800
_
9,327
SEDIMENT* (kg)
6
14
3
_
1,609
107
1,613
48
-
-
796
72
-
276
12
43
1
-
—
—
-
-
756
78
59
7
-
12
ATRAZINE LOSS * (g)
SEDIMENT
TRACE
0.008
TRACE
—
0.88
TRACE
0.79
TRACE
-
-
0.27
TRACE
-
0.07
TRACE
0.01
TRACE
-
_
-
-
-
0.04
TRACE
0.004
—
RUNOFF
17.3
0.411
4.53
-
17.4
87.2
14.4
44.0
-
-
3.07
9.3
-
0.81
1.21
0.20
0.08
—
-
-
-
-
0.41
0.007
0.06
—
TOTAL
17.3
0.42
4.53
—
18.28
87.2
15.9
44.0
-
—
3.34
9.3
—
0.88
1.21
0.21
0.08
-
-
-
-
—
0.45
0.01
0.06
-
—
* MEASURED
SIMULATED
106
-------
Table 7.
- Continued.
STORM DATE
AND
RAINFALL (cm)
23 JULY 73
(1.27)
25 JULY 73
(0.89)
28 JULY 73
(0.25)
31 JULY 73
(0.25)
1 AUGUST 73
(0.32)
6 AUGUST 73
(0.13)
14 AUGUST 73
(0.64)
17 AUGUST 73
(0.25)
18 AUGUST 73
(0.38)
31 AUGUST 73
(0.25)
3 SEPTEMBER 73
(0.36)
9 SEPTEMBER 73
(4.45)
10 SEPTEMBER 73
(0.76)
13 SEPTEMBER 73
(3.43)
14 SEPTEMBER 73
(0.81)
17 SEPTEMBER 73
(1.32)
27 SEPTEMBER 73
(0.51)
28 SEPTEMBER 72
(0.64)
31 SEPTEMBER 73
(1.37)
31 OCTOBER 73
(0.51)
RUNOFF* (I)
-
-
-
-
-
—
—
-
-
-
-
163,449
226,900
—
132,777
130,700
—
—
-
_
—
—
SEDIMENT* (kg)
-
—
-
-
—
—
-
-
-
-
-
89
6
-
83
4
-
-
-
-
-
-
ATRAZINE LOSS* (g)
SEDIMENT
-
_
—
-
—
-
-
-
-
-
-
—
-
-
—
-
-
-
—
-
RUNOFF
_
—
-
-
-
-
-
—
—
—
—
-
-
—
-
—
-
-
-
TOTAL
-
-
-
—
-
-
—
-
-
-
-
—
—
-
-
-
-
-
-
•MEASURED
SIMULATED
107
-------
Table 7.
— Continued.
STORM DATE
AND
RAINFALL (cm)
21 NOVEMBER 73
(2.08)
25 NOVEMBER 73
(0.84)
26 NOVEMBER 73
(0.13)
28 NOVEMBER 73
(1.27)
4 DECEMBER 73
(0.13)
5 DECEMBER 73
(3.86)
15 DECEMBER 73
(2.01)
20 DECEMBER 73
(2.62)
25 DECEMBER 73
(2.11)
29 DECEMBER 73
(6.33)
30 DECEMBER 73
(1.88)
31 DECEMBER 73
(5.38)
TOTALS
RUNOFF* (I)
-
-
-
-
—
11,010
52,000
-
49,062
33,100
8,050
-
13,188
422,236
150,000
2,356,474
1 ,876,846
SEDIMENT* (kg)
-
—
-
-
-
6
1
—
25
1
4.7
-
4
135
2
5,524.7
348.
ATRAZINE LOSS* (g)
SEDIMENT
-
-
—
-
—
-
-
-
-
—
—
2,071
TRACE
RUNOFF
-
-
-
—
—
-
-
—
-
-
—
36.761
163.63
TOTAL
-
-
—
-
—
-
-
—
-
-
—
38.83
163.63
'MEASURED
SIMULATED
108
-------
Elimination of the limiting term (L) in the model did not change
the simulated sediment loss on P-04. Further work will be
required to isolate the reasons why the sediment model, as
implemented in SCRAM, predicts unusually low values on P-04.
Although the simulated results are not in complete agree-
ment with the measured values of runoff, sediment loss, and
pesticide movement, the potential utility of simulation in
understanding and developing pesticide control methodologies is
evident. If the processes which effect the movement of pesti-
cides are understood, they can be expressed mathematically and
used to develop a model which in turn can be used to simulate
the behavior of the system under a variety of conditions. If
the model parameters are related to physical quantities which
can be measured in the laboratory, rather than empirical fitting
parameters, then new pesticide formulations can be "field tested'
via simulation against a variety of simulated experimental condi-
tions in a matter of hours.
The next section of this report contains sensitivity
analyses of each of the submodels. This section is presented
last because it is highly technical and of primary utility to
the SCRAM user rather than the average reader.
109
-------
SECTION VII
MATHEMATICAL MODELS AND SENSITIVITY ANALYSIS
SCRAM includes a number of mathematical submodels to simu-
late the complex natural phenomenon associated with the trans-
port, movement, and attenuation of pesticides in the environment.
Each submodel is modular; only the necessary inputs and outputs
are passed between submodels. At the present time there are six
submodels:
1. WATER: An infiltration/percolation model that predicts
the amount of runoff on the watershed during each event,
and the movement of water into the soil profile during
and after an event.
2. SED: A sediment model that predicts the soil erosion
process.
3. ADDE: An adsorption/desorption model that predicts the
simultaneous concentration of pesticide adsorbed and in
solution within the soil matrix.
4. DEGRAD: A degradation model that predicts the amount
of pesticide loss due to chemical and microbial processes.
5. VOLT: A model that predicts pesticide loss due to the
pesticide's volatile properties.
110
-------
6. EVAP: An evapotranspiration model that predicts water
loss due to net solar flux, vapor pressure gradient, and
plant metabolisms.
This section includes a discussion of the mathematical
equations which are the basis for each of the computer submodels.
A sensitivity analysis, performed on each submodel prior to
incorporation into SCRAM, is included within the discussion of
the model. The SCRAM user should read these sections carefully
before attempting to set up the simulation parameters.
WATER SUBMODEL AND SENSITIVITY ANALYSES
The general equations for describing flow in a nondeforma-
ble media may be derived by substituting the components of V
(seepage velocity) from Darcy's law into the equation of
23 24
continuity. ' The net result for water as an incompressible
fluid is:
H- = V • FRO) V*l (3)
o "C L- —'
where $ = total potential defined in terms of energy per
unit weight of water. Using this definition,
potential has the dimension of length and is
referred to as "head"
V = the gradient of total potential
R(6) = hydraulic conductivity
V = "del" or "nabla" is the vector differential oper-
ator.
Ill
-------
For purposes of simplifying the model we have only consider-
ed flow in the vertical direction (Z positive upwards). Equation
(3) then reduces to
M = i_ rK(0) M~| (4)
9t 9Z L 3ZJ ' [ '
The system is further simplified by neglecting adsorption
potential, chemical potential, osmotic - pressure potential, and
thermal potential. Total potential is then the sum of capillary
(hydrostatic-pressure only) and gravitational potential so that
= = h gives
30 _ 3 r 3(h-zn
it ~ yz LK(9) az J (6)
Differentiating
86 _ 3 I" 3hl 3K(9) , .
9t ~ TZ LK(6) TzJ ' ~JZ~ (7)
These equations assume a unique relationship between the
pressure or tension head h and moisture content 0. If this
assumption is valid it is possible to apply the chain rule of
differentiation to yield:
112
-------
at
/ <~\ Q V
where (~9h) = C = Specific Moisture Capacity.
Substituting into Equation (7) gives
8h _ 3 ( dh\ 9K(6)
C 3t ~ TZ VK(e)3 " ~
25
Using an adaptation of the Crank-Nicolson implicit method
for solving differential equations, the numerical form of
Equation (9) is:
At ~ 2(AZ)2cr1/2
2 AZ -
i-4-l i_i_i/ -i-j-i/y
(10)
where the subscripts "i" refer to distance and the super-
scripts "j" refer to time.
The procedure used to solve Equation (10) is similar to the
2 f\
technique of Hanks and Bowers and is outlined briefly below.
Compile tables which list moisture content 6 versus hydrau-
lic head, h, and diffusivity, D. Then proceed as follows:
. . ^ J_. ^ /A^x .035 AZ
(a) Estimate (At) J / = — -i-i/2
where I-' ' = infiltration rate during the previous time
step.
113
-------
J-l/2 _
"
Z DA0- £ DA0
J=8L 9=8L
evaluated at 0est - fe^ - 9? ^x 0.7 + 0j
(e) Compute h-? from Equation (10)
(f) Compute new 0-? from the corresponding h.-?
To implement the above procedure, Equation (10) is written
in matrix form with all terms multiplying h. on the left and the
' 1
terms multiplying h-?~ and the gravity terms on the right. The
resulting matrix on the left is tridiagonal and can be inverted
by Gauss elimination. There remains only the requirement for
initial and boundary conditions.
Initial conditions of 0 for all depths are specified by
the modeller or are based upon his knowledge of soil conditions
at the start of the simulation time period. The effect of im-
properly specifying the initial soil moisture profile is a complex
function of the soil type, evapotranspiration model, and nature of
the first storm. Generally, if the simulation is not started on
114
-------
the day of a big storm, little or no impact will result if the
evapotranspiration model is functioning properly. Usually a
period of dry soil can be picked to facilitate the choice of
initial 9s. If the last profile is available from the simula-
tion output it can be used to restart the simulation.
Figure 64 shows a representative soil column used for solving
Equation (10) as outlined above. The top layer is the rainfall
and runoff layer and ordinarily should have an initial value of
zero, i.e., no standing water.
The depth of the soil profile, NEND-1, is a simulation
parameter which determines where water transfer to lower zone
storage occurs. Water reaching this layer is transferred to
lower zone storage immediately. Thus the value of 6 at the
lower boundary does not change with time.
Equation (10) contains two terms on the right hand side.
The first term represents the movement of water between the soil
layer immediately above the i layer (i-1) and the i layer
itself. The second term represents the movement of water
between the i layer and the layer immediately below (i + 1).
For i = 2, i.e., the first soil layer, the boundary condition is
specified by setting the first term equal to zero. Thus water
is not allowed to move into the top layer during a time step At
via any interaction between the i = 1 and i = 2 layers. Instead
the amount of rainfall during At is inserted before the time
solution to Equation (10) is determined. In effect this will
allow a small amount of water to move into the 2nd soil layer
during At. The error is not significant because At is small.
At the lower boundary, water is not permitted to move
during At between the i = NEND layer and the NEND + 1 layer.
That is, the second term on the right side of Equation (10) for
115
-------
02
0 NEND
N=1 RAIN AND RUNOFF LAYER
N = 2 1ST SOIL LAYER
N = 3
N = 3
N = NEND [(NEND- 1) SOIL LAYER]
LOWER ZONE STORAGE
Figure 64.
Representative soil column for water
movement and storage.
116
-------
i = NEND is set equal to zero. However, water does not build up
in this layer because any water which enters the layer is trans-
ferred to lower zone storage.
Sensitivity Analysis of WATER Submodel
Three storms were selected from the 1973 (P-01) data to
use while testing the infiltration submodel sensitivity. Strictly
speaking, it is not necessary to use actual storms, but the
sensitivity of the submodel should be tested within the range of
actual rainfall rates. In addition, the results of the sensitiv-
ity runs can also be used to set parameters for the final simula-
tion if the actual storm data is used. Table 8 summarizes the
rainfall data for the three storms selected.
The first event (May 28, 1973) represents a relatively
short storm of high intensity over the entire period. The
second event (September 9, 1973) is of moderate intensity over
a longer period and exhibits two peak rainfall rates. The third
event (December 31, 1973) is a low intensity storm over a long
period with a short peak rainfall rate.
Table 8. RAINFALL CHARACTERISTICS FOR THREE STORMS IN 1973
Storm
Total 1st
Rain Duration Hour
Peak
Rainfall
Rate
May 28, 1973 5.4 cm 85 min
December 31,
1973 5.0 cm 490 min
Peak
Duration
4.2 cm 0.14 cm/min 5 min
September 9,
1973 4.1 cm 138 min 2.4 cm 0.12 cm/min 5 min (twice)
0.5 cm 0.233 cm/min 3 min
117
-------
Sensitivity to Rainfall Characteristics by Soil Type
A soil type is defined by a unique set of soil diffusivity
values and moisture potential values as a function of soil
moisture content. Hydraulic conductivity is calculated from
the tables of diffusivity and moisture potential as discussed
above. Representative values of pressure head and diffusivity
are shown in Figures 65 and 66.
Four soil types were tested for each of the storms:
(1) Geary Silt Loam,26 (2) Sarpy Loam,26 (3) Light Clay,27
2 8
and (4) Cecil Sand. Sensitivity to soil type is illustrated
by comparing the runoff hydrographs for each soil type for a
given storm (Figures 67, 68, and 69).
Initial moisture content was taken as dry (9 between 0.06
and 0.07) throughout the soil profile. The results are not
surprising. Clay produces the most runoff and exhibits the
greatest sensitivity to rainfall rate. Geary produces runoff
but considerably less than Clay. Sarpy and Cecil produce little
or no runoff. Table 9 summarizes the results and presents the
measured values for watershed P-01.
Detailed comparisons between the actual and simulated
data are not appropriate because the initial moisture profile
was arbitrarily selected. However, the absence of any runoff
is significant for the first two storms because the choice of
initial moisture content is realistic.
118
-------
i//vs. e
DC
LU
<
E
n
c.
K
LU
6
o.
HI
X
Z>
(A
O
-10
0.1 0.2 0.3
VOLUMETRIC MOISTURE CONTENT 6
0.4
0.5
Figure 65. Moisture potential for selected soil types
119
-------
1.0
10"
10
-2
O
in
10
-3
t/5
0 10-4
10
-5
10
-7
D VS. 6
-v°£
0.1 0.2 0.3 0.4
VOLUMETRIC MOISTURE CONTENT 6
0.5
Figure 66. Diffusivity for selected soil types
120
-------
23200
LIGHTCLAY
52 78
ELAPSED TIME (MINUTES)
104
130
Figure 67.
5
cc
14300
11440
8580
5720
2860
P-01 Watershed: WATER model sensitivity to soil
type for May 28, 1973, storm
30
60 90
ELAPSED TIME (MINUTES)
120
150
Figure 68
P-01 Watershed: WATER model sensitivity to soil
type for September 9, 1973, storm
121
-------
^-
D
cc
UJ
19100
15280
11460
7640
3820
LIGHT CLAY
GEARY SILT LOAM
SARPY LOAM
102 204 306
ELAPSED TIME (MINUTES)
408
510
Figure 69.
P-01 Watershed: WATER model sensitivity to soil
type for December 31, 1973, storm
Table 9. RUNOFF VOLUME (LITERS) BY SOIL TYPE
Storm
Date
May 28, 1973
September 9,
1973
December 31,
1973
Runoff Volume in Liters
Actual Clay Geary
803,670 1,033,785 383,614
400,461
475,000
Sarpy/Cecil
720,416 174,123
583,054 129,711 2703
122
-------
Sensitivity to Soil Layer Thickness
Three sets of computer runs were made to test the sensitivi-
ty of the model to the thickness of the soil layers (AZ in Equa-
tion 10 and G in the computer code). The impact of G can show up
in two ways; (1) an indirect effect via the treatment of the
upper boundary condition, and (2) an indirect effect via a
change in the simulation time step.
Changing G produces a significant effect on the simulated
runoff. For Clay, as G increases from 0.5 to 2.0 cm. the total
runoff tends to decrease except for the December 31, 1973,
storm (Table 10). Runoff is decreased for the May 28, 1973,
and September 9, 1973, storms because of the effect on the bound-
ary condition at the surface. The apparent anomaly in the
December 31, 1973, storm is caused by an indirect effect via the
lower boundary condition and should be ignored (see Figures 70 to
72).
Table 10. RUNOFF VOLUME (LITERS) AS A FUNCTION
OF SOIL LAYER THICKNESS FOR CLAY SOIL
Runoff volume in Liters
Storm
Date G=0.5 cm G=1.0 cm G=2.0 cm
May 28, 1973 1,052,675 1,033,785 946,027
September 9, 1973 720,015 706,416 633,107
December 31, 1973 403,710 538,054 470,544
A slightly different effect was observed when the model was
tested for Geary Soil (Table 11, Figures 73 to 75). Water moves
through the soil profile rapidly for Geary and the corresponding
sensitivity runs actually demonstrate the effect of the lower
boundary condition. For a fixed number of soil layers, water is
123
-------
2410 r
° 1928 -
ai
H
D
CO
DC
LU
1446 .
g 964 -
482 -
28 56 84
ELAPSED TIME (MINUTES)
112
Figure 70.
1510
° 1208 -
CO
en
LU
906
604 -
302 -
WATER model sensitivity to soil layer thickness
(G) for Clay soil (May 28, 1973, storm)
30 60 90
ELAPSED TIME (MINUTES)
120
Figure 71.
WATER model sensitivity to soil layer thickness
(G) for Clay soil (September 9, 1973, storm)
124
-------
2010
LU
H
ts>
DC
LU
o
1608
1206
804
402
G = 0.5cm
G = 2.0 cm
102 204 306
ELAPSED TIME (MINUTES)
408
Figure 72.
WATER model sensitivity to soil layer thickness
(G) for Clay soil (December 31, 1973, storm)
LU
D
in
CC
W
3
O
1510 r
1208 ~
906 -
604
302 -
Figure 73.
G = 0.5 cm
G = 2.0 cm
G = 1.0 cm
28 56 84
ELAPSED TIME (MINUTES)
112
WATER model sensitivity to soil layer thickness
(G) for Geary soil (May 28, 1973, storm)
125
-------
901 Or
128
ELAPSED TIME (MINUTES)
Figure 74.
1010
808 -
606
vs
CC
O
404
202
WATER model sensitivity to soil layer thickness
(G) for Geary soil (September 9, 1973, storm)
40 80 120
ELAPSED TIME (MINUTES)
160
Figure 75.
WATER model sensitivity to soil layer thickness
(G) for Geary soil (December 31, 1973, storm)
126
-------
removed and transferred to lower zone storage more rapidly as G
decreases. Hence, for short duration storms runoff increased as
G increases, but for a long duration storm (December 31, 1973)
this effect is nullified and the runoff decreased as the soil
layer thickness increases.
Table 11. RUNOFF VOLUME (LITERS) AS A FUNCTION
OF SOIL LAYER THICKNESS FOR GEARY SOIL
Runoff Volume in Liters
Storm
Date G=0.5 cm G=1.0 cm G=2.0 cm
May 28, 1973 269,746 383,614 419,806
September 9, 1973 126,719 174,123 164,165
December 31, 1973 132,176 129,711 119,592
In summary, as G increased from 0.5 to 2.0 cm, the runoff
decreases due to the effect on the upper boundary condition.
For soils with high infiltration rates, G should be set at or
above 2.0 cm and NEND should be 15-20. Soils with low infiltra-
tion, like Clay, will be very sensitive to G and numbers less
than 1.0 cm should be specified.
Sensitivity to Initial Moisture Content
The significance of the initial moisture content on the
runoff hydrograph will depend on the soil type. Soils which
exhibit high infiltration and percolation rates will not exhibit
much sensitivity to the initial soil moisture profile (assuming
the soil is not saturated).
127
-------
Sensitivity to initial moisture profile was tested for each
of the three storms using Clay and Geary soils. Dry (0 = 0.06),
moist (0 = 0.20), and wet (0 = 0.35) soil profiles were tested.
Figures 76 to 78 show the effect on the runoff hydrograph
for Clay. Similar less dramatic changes were observed for Geary
soil in Figures 79 to 81.
For both the May 28, 1973, and September 9, 1973, storms,
the runoff volume for Clay increased approximately 10 percent
when 0 was changed from 0.06 to 0.20 and increased another
12 percent when 0 was changed from 0.20 to 0.35. The effect
was more significant for the December 31, 1973, storm because
of the high intensity rainfall that occurred.
These results suggest that the runoff hydrograph is not
particularly sensitive to the initial soil moisture profile
unless there is a period of high intensity rainfall.
Specification of Boundary Conditions
The boundary conditions must be specified in order to
solve Equation (10). That is, the modeller must supply values
f°r hr hNEND + 1 and 8NEND + 1'
Infiltration from a flooded surface may be represented by
having h-? set to zero. This situation may occur at some time
during the storm, but it would not be true generally during the
early part of the storm. Accordingly, more water would be infil-
trated during a short time step than the amount that actually
fell on the ground. An adjustment would have to be made after
each time step to correct the water in the first soil layer in
a manner which is consistent with the rainfall rate during that
period. It would also follow that if the flooded infiltration
rate were less than the rainfall rate, runoff would occur regard-
less of the moisture content of the first soil layer.
128
-------
D
Z
to
oc
2750
2200 -
1650 _
d 1100 .
O
550 .
28
56 81
ELAPSED TIME (MINUTES)
112
WATER model sensitivity to initial soil moisture
for Clay soil (May 28, 1973, storm)
62 93
ELAPSED TIME (MINUTES)
124
Figure 77-
WATER model sensitivity to initial soil moisture
content for Clay soil (September 9, 1973, storm)
129
-------
tr
LU
I-
o
2420 p-
1936
1452
968
484
Figure 78.
33700
26960
108
= 0.06
216 324
ELAPSED TIME (MINUTES)
432
540
WATER model sensitivity to initial soil moisture
for Clay soil (December 31, 1973, storm)
CC
LU
I-
o
20220
13480
6740
28
56 84
ELAPSED TIME (MINUTES)
112
140
Figure 79.
WATER model sensitivity to initial soil moisture
for Geary soil (May 28, 1973, storm)
130
-------
UJ
I-
D
CO
cc
LLJ
O
18850 i-
15080
11310
7540
3770
32
0 = 0.35
64 96
ELAPSED TIME (MINUTES)
128
160
Figure 80.
WATER model sensitivity to initial soil moisture
for Geary soil (September 9, 1973, storm)
UJ
I-
D
Z
in
DC
O
22450
17960
13470
8980 _
4490 -
Figure 81.
40
120
ELAPSED TIME (MINUTES)
160
200
WATER model sensitivity to initial soil moisture
for Geary soil (December 31, 1973, storm)
131
-------
Another possible method of setting the upper boundary
condition would be h? = constant, but not equal to zero. In
effect this would limit the infiltration rate to be less than or
equal to some number that depends on the choice of the constant.
The maximum moisture content of the first soil layer could
never exceed 6(h^).
For selecting the upper boundary condition, each method
above is basically unsatisfactory because they do not correspond
to realistic expectations. During rainfall on a soil where the
moisture content is below saturation, the moisture content at
the surface should build up gradually until saturation is
reached or until the rainfall ceases, whichever occurs first.
For this reason the upper boundary condition is defined as
follows:
(1) For a small time interval (<_1 minute) calculate
the rainfall that would occur
(2) Add the rainfall to the first soil layer
(3) If the first soil layer exceeds saturation, the
excess is runoff
(4) Solve Equation (10) without letting any additional
water infiltrate.
Actually, the simulation structure is more complex, since
any zone within the watershed may contain water which has run off
in the previous time step. Step (1) therefore includes any water
on the surface from the previous time step which has not run off,
in addition to that water which has run onto a zone from another
zone.
132
-------
As a result, the specification of the upper boundary condi-
tion is fixed by the constraint that the infiltrated water
must be less than or equal to the total rainfall at a given time.
The excess of rainfall over cumulative infiltration is runoff
for each zone.
At the lower boundary the situation is different. For soils
which have high infiltration and percolation rates, the water can
easily move 10-20 cm into the soil during a storm of moderate
duration. Once the wetting front has reached another soil hori-
zon with lower permeability the water will back up, reducing the
infiltration rate.
Using Geary Silt Loam as a test case, percolated water
was allowed to build up in the "NEND" layer. For the May 28,
1973, storm this condition generates 519,546 liters of runoff.
This can be compared to a boundary condition of transferring any
water that reaches the "NEND" layer to lower zone storage which
produces 383,614 liters of runoff.
If "NEND" is set large enough the water will not penetrate
to the bottom layer during the rainfall event. Using this condi-
tion the simulated runoff was 419,806 liters. This approach is
satisfactory if the choice of NEND does not require going below
the next soil horizon.
Another possibility would be to let the soil moisture
content build up to a specified level and then remain constant
by transferring water to the lower zone storage. This approach
increases runoff as the specified level is increased until the
519,546 liter figure is reached.
Based upon the results discussed above, the lower boundary
condition is specified to minimize the impact on runoff. NEND is
set between 15 and 20. When a soil layer thickness is 1.0 cm,
water generally will not infiltrate to this depth during a
133
-------
typical storm. Water that does reach this point, either during
the storm or during subsequent percolation, is transferred to
lower zone storage. This will produce some runoff error in
the long duration storms but it should not be significant over
a one year period.
SEDIMENT TRANSPORT SUBMODEL (SED) AND SENSITIVITY ANALYSES
The SCRAM simulation structure requires a microscopic
description of sediment yield for the upland phase of the soil
erosion process. The upland phase is closely related to the
individual precipitation events and the mechanics of these
events are important in determining the actual yield.
Generally, upland erosion is categorized as either rill or
interrill erosion. In rill erosion the runoff on an erodible
soil surface concentrates into many well defined small irregular
channels called rills. The erosion occurring on the area between
the rills is called interrill erosion.
For these areas the erosive agents are rainfall and runoff.
Consequently, the mechanics of sediment removal and transport
are describable by four different processes:
(1) detachment by rainfall (raindrop impact)
(2) transport by rainfall (raindrop splash)
(3) detachment by runoff
(4) transport by runoff.
Factors which must be considered in describing the yield
from these processes include:
134
-------
(1) Soil properties - soil type, texture, tilth, soil
moisture content, permeability, compactness, and
infiltration capacity. These conditions influence
the amount of runoff and the soil behavior when
subjected to rainfall impact and moving water.
(2) Vegetation properties - type of vegetation, primar-
ily as it effects the amount of rain reaching the
ground and the kinetic energy of the rainfall
reaching the ground.
(3) Topographic properties - slope, slope length,
average width.
(4) Human influencing properties - agricultural
practices.
(5) Meteorological properties - primarily the amount,
duration, and intensity of rainfall.
It is a difficult task to assemble a mathematical model at
the micro-level which includes all of the variables and param-
eters and describes the physics of the transport. Part of the
difficulty is in describing the intricate relationships involved
and in being able to quantify and measure values needed in order
to complete the description.
A search of the literature revealed several incomplete but
likely candidates. These models included stochastic sediment
29 TO
yield models, ' models using kinematic wave theory (continuity
135
-------
and dynamic equations), conceptual models for computer simula
tion, and models such as the Foster-Meyer ' ' which
combines conceptual techniques with fundamental continuity
equations.
The Foster-Meyer model was selected for use in SCRAM
because it incorporates parameters which are available to or
generated during the simulation. Conversely, the model has not
been tested against field data and consequently the model param-
eters have not been developed or related to measurable soil
properties and characteristics. Some of these difficulties
were overcome with the assistance of Mr. Foster.
Foster-Meyer Sediment Model
The development of the Foster-Meyer (F-M) sediment model
starts with the basic continuity-of-mass transport equation:
9GF
DF + Di = 83T
where D = rill flow detachment (deposition) rate at a
location (wt/unit area/time)
D. = delivery rate of detached particles from
interrill areas to the rill flow (wt/unit
area/time)
GF - sediment load of the flow at any location
on a slope; weight transport rate (wt/unit
width/time)
Deposition is viewed as the negative of detachment.
136
-------
GF is the independent variable of interest. To determine
values for it, an interrelationship equation is used involving
flow detachment and the weight transport rate:
DF GF
where DC = detachment capability of the rill flow at a
location (weight/ unit area/time)
TC = flow transport capability at a location (weight/
unit width/time)
34
Foster and Meyer caution that Equation (12) above has not been
•3 /• O C
experimentally verified, however, Bennett, Foster and Meyer
present a qualitative argument for its usage.
As for the other terms needed to solve for Gp, Foster and
34 35
Meyer ' cite empirical evidence as a basis for assuming that
both D and T are proportional to a power of the bottom sheer
3/2 3/2
stress (D a T ' , T a T ' , where T is the tractive force or
l^, \— •
bottom sheer stress). On the basis of empirical evidence, the
D. term in Equation (11) has been shown to be approximately
1 2
proportional to the square of the rainfall intensity (D^ <* I ,
where I is the rainfall intensity- )
Except for the evaluating coefficients and proportionality
constants involved in the terms above, Equations (11) and (12)
can be solved given knowledge of the rainfall conditions and the
overland flow.
137
-------
Sediment Model Output
The Foster-Meyer model predicts the following quantities
(1) Sediment load at any location on the slope (weight/
unit area/time) and total sediment "yield" at the
bottom of the slope.
(2) Detachment/deposition rate at any location on the
slope (weight/unit area/time).
(3) Deposition and sediment load decay beyond the end
of the slope (weight/unit area/time).
Derivation of Working Equations
For convenience the equations describing the processes
being modeled (11) and (12) are repeated here as a single
equation.
F
(13)
Dc Tc =
Initial conditions at the top of the slope are assumed known or
determinable.
The solution of Equation (13) parallels that of Foster and
34 35
Meyer ' and employs the following notation:
138
-------
let
L
X
D
T
X,
CO
?CO
length of the slope (reference)
distance from the top (down the flow)
detachment capacity at the bottom of the slope
transport capacity at the bottom of the slope
X/L so that 0
Figure 82.
Schematic of upland area used to develop
Foster-Meyer sediment model
139
-------
Next, define non dimensional detachment capacity as
g* = 5- = VITS^ = x*s*
co
where C_ = Coefficient of flow detachment capacity and
C (1) refers to the bottom of the slope.
Coefficient of flow transport capacity and
C (1) is the corresponding term at the bott
of the slope.
~ Relative slope along a land profile.
Next set:
LD.
6 = — — (a measure of the T that is filled by
CO
interrill rainfall detachment and transport)
LDCO
and a = - - (a measure of the flow's capacity to detach
CO
a certain soil),
substituting into Equation (13) and reducing:
140
-------
(d VDco)
dX,.
(VDCO)
dx~
CO
- DF/DCO
(16)
Solution for a Constant Slope
For a single uniform slope, S(X) = S , and making the
reasonable assumption that D. = constant, we can integrate
Equation (16) to give:
CO
+ C e~aX*
(17)
where C is a constant of integration and must be evaluated by
initial conditions, viz., perhaps D (0) = 0, and
T
= X,
CO
D
(18)
CO
Solution for the general case -
Assuming it is possible to "come straight down the
slope" as depicted in Figure 82, the general solution is derived
as follows:
let
0
S .
reference slope
slope of the j increment; j = 1 at the top
141
-------
let
K.
S*. = S./S for the j interval; then
J J
D
CO
a
.
C.e~aX* (19)
and
= K .:
(20)
CO
where
is now relative to the jth interval
Evaluation of the integration constant C .
For the top interval, the initial conditions at the top of
the slope are needed as before, viz., D (1) (0) = 0. Once
having gotten started, C. has the form
C. =
K. -
D
|l-e-aX*y
CO
aX,
(21)
where X^ is at the upper end of the j increment or the
s t
lower end of the j-1
Also note that the following condition must hold:
n
D
- = K'X
(22)
co
co
142
-------
Equation (21) however allows D to be continuous as the
F
transition is made from the (j-1) to the (j) interval. In
practice, we first maintain the continuity of the sediment load.
GJ)(X = GJ~1) (23)
and from Equation (20)
F
~ = - - - - ^ (24)
i T
CO > U CO
Substitution into Equation (19) produces the new C..
However, if D (X^ ) is negative, deposition is occurring.
In this case, if the slope increment is long enough, deposition
may cease and erosion may reoccur at a lower position.
The equations describing deposition are:
DF(J)(XJ = ^
DCO = a
(25)
Tco -1 co
for
interval
(26)
143
-------
where
c. =
D
co
-aX.
- e *
ry (27)
Also, since 0^=0 where deposition ends, say, at X , solve for
r "
X to give
e ^
= ± £n(K. -
Therefore, at X+ = X compute a new value of C. :
* e 3
and proceed for
'K. -
a
' -aX \ aX
1 - e e l e e
(28]
The procedure to evaluate constants to reduce accumulated
error is:
(1) Evaluate constants at each slope change
(2) Evaluate constants where D^ = 0
r
(3) Evaluate constants where deposition ends and
detachment begins.
Model Parameters
The delivery rate of detached particles from interrill
areas to rill flow, D., is a required model parameter. In
certain situations D. is assumed constant, e.g., uniform slope
1 32
and constant rainfall rate. In general, Meyer and Wischmeier
have demonstrated that D. is proportional to the square of
rainfall intensity. In particular:
Di =
(29)
144
-------
where I = rainfall intensity
K_ = function of the soil type
A^ = area of the increment under observation
This approach has been adapted by ESL for use in the Foster-Meyer
Sediment Model.
Estimation of the detachment capacity of the rill flow
at a location, D , is more complicated. According to Yalin,
v_*
sediment motion begins when the lift force of the flow exceeds a
critical lift force. Once the particles are lifted from the bed,
the drag force of the flow carries particles "downstream" until
the particle weight forces it out of the flow and back to the bed.
The average critical force for a number of agricultural soils
appears to be about 1.0 g/cm.
For large tractive forces (t»1.0 g/cm)
D ocT (30)
\^
in general
D = c , T3/2 (31)
c d
T = c. T3/2 (32)
c t
where C = coefficient depending on particle size and density
C, = coefficient that is a function of soils resistance
d
to erosion by flow
In the Foster-Meyer model ' the average sheer stress
is defined as:
145
-------
T = yyS
where y = density of runoff
S = slope
y = average flow depth
and where y and S are functions of X.
A more exact expression for bottom sheer stress is
T =
where R, = hydraulic radius
S = slope of the energy gradeline
Because of the small flow depths one can assume S = S (S
\2
is the slope of land profile at X) , and assuming turbulent flow,
then flow depth = hydraulic radius (since the width of the
flow » depth) . Hence the expression
T = YyS (33)
39
By the Chezy form of the uniform flow equation the
average flow depth at location X is:
2/3
y= [a-X-1- (8g S/f)1/2J (34)
where a = excess rainfall rate = (rainfall intensity
infiltration rate)
S = slope at X
g = acceleration constant due to gravity
f = Darcy-Weisbach coefficient of friction
146
-------
The effective tractive force (bottom sheer stress) is then
proportional to T.
T - CrpT (35)
so that
3/2 3/2 f 1//2
Dc = Cd ' Crp * l' (f?> • S • a - X (36)
and
T = C - C3/2 - Y3/2 (f_) 7 ' S • a • X (37)
c t rp Y (8g}
Slightly different estimates for D , T can be derived in terms
C-- O
of XA. As noted by Foster and Meyer, ^5 the estimates of D and
O
T may be modified using discharge rates rather than excess rain-
C
fall measures .
c
and
3/2 f 1/2
D = C(CY) (] ' S ' X* ' q = CSX* (38)
where C = coefficient for flow transport
Cn = coefficient for flow detachment
q - discharge rate per unit width at the bottom of
the slope
X* - X/L
147
-------
Within the SCRAM simulation structure the average flow
depth is generated in the WATER subroutine. Accordingly, we can
combine Equations (31), (32), (33), and (35) to write:
Dc = Cd(Crp
- K2(yS)3/2 (40)
and Tc = Ct(Crp6yS)3/2
= K1(yS)3/2 (41)
Sediment Model Parameter Estimate
The first parameter of interest is K_, used in calculating
the delivery rate of detached particles from interrill areas to
rill flow:
D. = K3I2
where I = rainfall intensity.
To obtain "ball park" estimates for K_, data from Molden-
41
hauer and Long were utilized. The Moldenhauer data were ob-
tained in laboratory experiments and are summarized in Table 12
2
below; the area of the test "beds" was 1394 cm , the units have
been changed to the metric system, and the K_ calculations have
been added.
148
-------
Table 12. EXPERIMENTAL VALUES FOR K .
Rainfall Rate
Soil Type
1. Liton Silty
Clay
2. Marshall Silty
Clay Loam
3. Ida Silt
4. Kenyon Loam
5. Hagens Fine Sand
I = 9.527 x 10
-4
cm/sec
Di(observed) K3
g/cm2/sec Calculated
1.72 x 10
-5
-5
18.94
1.43 x 10
5.02 x 10~6
9.54 x 10~6
15.75
5.5
10.5
-4
I = 18.833 x 10 cm/sec
Dj_ (observed) K3
g/cm^/sec Calculated
4.59 x 10
-5
3.2 x 10
-5
2.25 x 10
2.3 x 10~5
-5
2.55 x 10
-5
12.93
9.0
6.34
6.48
7.18
The proposed relationship is not exactly satisfied for the
Moldenhauer data, but it does suggest a range for K.. between
7 and 20.
Similarly, if we use the data from Foster and Meyer
34,35
shown in Table 13, a range for K between 15 and 20 is derived.
- 3/2
As noted above, the detachment capacity D = K,, (yS) '
3/2 -
and the transport capacity T = K,(yS) ' , where y is the average
flow depth at location X and S is the profile slope at X.
Ranges for K, and K2 were estimated from the Foster-
Meyer data in Table 13.
L = 35 feet.
T was calculated from 6 = LD^/T with
Then D was determined from the relation
\-f\~S
a = L Dco/Tco-
149
-------
Table 13. PREDICTED VALUES OF SEDIMENT
LOAD FROM FOSTER AND MEYER34'35
G (1) D Soil Loss
Case a 6 T tons/acre/hr Predicted Measured
tons/acre/hr tons/acre/hr
1. .046 057 0784 10.0 13.7 11.5
2. .250 .029 .1409 7.7 37.2 29.0
3. .065 .043 .0734 7.7 12.3 10.9
The calculated values for T were 1.164, 1.762, and
1.188 g/cm/sec for the three cases shown in Table 13. Corres-
-5 -4
ponding values for D _ were 5.025 x 10 , 4.127 x 10 , and
-5 2
7.23 x 10 g/cm /sec.
Apparently, Table 13 contains an error because the program
predicted the same values for the first two cases, but predicted
13.14 tons/acre/hour for the third case.
With the above values as representative for T and D ,
I— \^
K, was initially estimated to be in the interval (20,300) and K,,
-4-2
in the interval (8.5 x 10 , 8 x 10 ). The smaller values appear
to be better under the steady state and uniform rainfall excess
assumptions.
Based upon the results and the sensitivity analysis in
the next section, a suitable range of parameters can be developed
for running the simulation.
150
-------
Sensitivity Analysis of the Sediment Model^
The general sensitivity of the sediment model to variations
of the several different parameters was checked analytically
where possible and also via computer runs to obtain numerical
estimates. For the analytical determinations the solution for
the sediment load reduced to its most basic form is:
Gp(Xw)=(K1C1)
- e
~L
(42)
where
S)
3/2
and the other notation is as used previously. (Note that 6=1.)
This form is used and discussed further below.
For the computer checkout the following inputs, with
their assigned values shown, were used for tests. Except for
length and width of the slope, which remained constant through-
out the testing, each of the inputs were allowed to vary while
all others remained fixed.
Length
Width
Slope
Average Runoff Depth
Rainfall Intensity
K3 (Soil Type Constant)
Kl
K2
405 m
670 m
.0375(3.75%)
. 5 cm
.1 cm/min
8.
20.
1. X 10
-3
151
-------
These values of length, width, and slope were chosen to approxi-
mate the dimensions of the P-01 watershed. Average runoff depth
and rainfall intensity values were chosen after studying rainfall
data on P-01 as reasonable values during a storm. The values
chosen for K_, K,, and K_ are discussed in the previous section.
Sensitivity to slope -
A plot of sediment load vs slope of the watershed is
shown in Figure 83. Slope was allowed to vary from 1% to 30%.
The model is not very sensitive to changes in slope in the ranges
of interest although, as expected, sediment load always increases
as slope increases.
From Equation (42) above, the sediment load for only S
3/2
variable has the form G_ = N,S ' + N0, where the Ns are con-
r L z
stants. This increasing function has the form noted in the
figure.
Sensitivity to rainfall intensity -
Figure 84 shows that the model is relatively sensitive
to rainfall intensity. As expected, sediment load is always an
increasing function of rainfall intensity. The curve is not
linear, as the rainfall intensity term is squared in the model
equations. Analytically, for only I variable the sediment load
2
has the general form Gp = A + BI .
If I = 0, i.e., rainfall has stopped, then
(43)
152
-------
20
o
X
o
UJ
16
12
o
1-
z
UJ
§
O
ill
05
12
PERCENT SLOPE
18
24
30
Figure 83,
Sensitivity of sediment load to slope
40
32
X
O
UJ
o
ir
(D
a
o
i-
z
UJ
5
Q
UJ
24
16
12 18
RAINFALL INTENSITY (CM/MIN) X 100
24
30
Figure 84.
Sensitivity of sediment load to rainfall intensity
153
-------
where
therefore: unless S = slope = 0, sediment load decreases con-
tinuously until y, the average depth, reaches zero.
Sensitivity to position on the slope
In Figure 85, sediment load was computed at 100 points
on the slope, beginning near the top and moving downward to the
bottom of the slope. This test was made primarily to show that
the model behaves reasonably well when the slope is cut into
pieces; this is necessary to determine when and if a new con-
stant of integration needs to be calculated.
Sensitivity to the number of increments down the slope -
In Figure 86 the sediment load was calculated when the
slope was divided into 1, 10, 50, and 100 equal area segments.
If the slope is divided into n equal area segments, the computer
model checks n times to see if it is necessary to calculate a
new constant of integration. The plot in Figure 86 shows that
sediment load remains constant, regardless of the value of n, at
least for the given input conditions.
Sensitivity to rainfall detachment parameter K3 -
Using Equation (42), it is easy to show that G = A + BK
for all parameters except K^ constant. B is always greater than
zero, and hence G is a linear increasing function of K-. This
relationship is verified by the computer analysis shown in Figure
87 where K_ was allowed to vary between 1 and 24.
154
-------
70
o
o
o
111
o
ir
56
42
o
_J
Q
111
UJ
28
14
Figure 85,
70
56
o
UJ
o
£ 42
I-
ijj 28
14
22 44 66 88
POSITION ON THE SLOPE (100 AT THE BOTTOM)
110
Sensitivity of sediment load to length of the
slope
22
44 66
NUMBEft OF INCREMENTS
88
110
Figure 86.
Sensitivity of sediment load to the number of
subdivisions down the slope
155
-------
Sensitivity to detachment capacity parameter K2 -^
K~ is a complex parameter used to help estimate the detach-
ment capacity of the water flow. As previously noted, K^
includes a measure of a soil's resistance to erosion by flow
and a proportionality constant obtained by calculating the
tractive force of this flow from the average sheer stress.
For fixed X., and only allowing K0 to vary, the sediment
x Z.
load function can be written as :
K12C1(l-9) , x _
**
K
2
so that
-WK
Klcl(i-e)
W Ke
4^)1
-'
(45)
where W = —— > 0
1 ~
-WK-
and WK2e + e z - 1 £ 0.
For 0<6< 1, ^-i- > 0
and hence G is an increasing function of K?;
for 6>1, -^—r— < 0 and hence G,., is a decreasing function of K0,
oA0 F 2
156
-------
Figure 88 shows sediment load is K~ for four different values
of K^. The function is increasing for values of K, which make
0<1, and decreasing for values of K which make 9>1. The
physically meaningful values seem to occur for the case 0<9<1.
Sensitivity to the transport capacity parameter Kj_ -
K^ is a parameter used to help estimate the transport
capacity of the water flow. As previously noted, K, is a complex
parameter which is a function of soil particle size and density
and includes a proportionality constant relating average sheer
stress to the tractive force of the flow.
An analytical expression for the sensitivity of G to K,
is difficult to develop but G is an increasing function of this
parameter.
Figure 89 shows sediment load as a function of K,. Four
curves were plotted, each with a different value of K~ (constant
associated with detachment capacity). All four of the curves
intersect where 9=1. (See model description.) The sensi-
tivity of the model to K, shows a marked dependency on the value
of K2 because the ratio K /K, appears in the equation for G .
For the special case of 9 = 1, Gp = K-^X* = KI (y S)3/2
and so all of the curves will intersect.
Sensitivity to average runoff depth -
With all other coefficients remaining fixed (except y),
the sediment load equation has the form:
K0y
3/2
X* -
(46)
157
-------
20
o
X
o
LLI
(f)
i
o
ir
a
a
<
o
16
12
z
LU
Q
LU
12
18
24
30
Figure 87.
o
X
O
LU
C/3
O
It
(D
Q
O
_J
1-
z
HI
D
LLI
t/3
Sensitivity of sediment load to the constant,
K., = ST associated with rainfall detachment
100
Figure 88
Sensitivity of sediment load to the constant,
K2 associated with rill flow detachment
capability
158
-------
it is easy to show that
.3/2
3y
r
K.
K
I
L -^- T
x '
3/2y
1/2
>0
hence G is an increasing function of y.
It can be seen in Figure 90, where sediment load vs average
runoff depth is plotted, that the model is extremely sensitive
to runoff depth. In order that actual conditions can be more
realistically simulated, it is important to take small time steps
to keep the runoff depth low enough to simulate actual conditions,
Sensitivity to vegetation parameters -
As coded for the sensitivity analyses, the sediment
model does not directly take into account the particular vegeta-
tion or mulch type(s) present on the watershed subplots. Foster
and Meyer studied certain aspects of this problem, e.g., measure-
ments with straw and wheat mulch, and suggested that the ratio of
the "unmulched" sheer stress of the flow to that of the "mulched"
was a constant raised to a power. The constant was the cube of
the ratio of the average flow velocity with mulch to that of the
unmulched flow - all other conditions being the same.
With no data available on this aspect of the problem,
it was decided not to modify the model during the sensitivity
tests to try to account for the "vegetation" type parameters.
159
-------
o
X
0
LU
in
DC
e>
o
<
o
z
LU
Q
LU
90 r
72 -
54
36 -
18 -
164
246
328
410
Figure 89,
o
X
G
LU
(fi
O
ir
Q
o
Q
111
(/l
95
76
57
38
19
Sensitivity of sediment load to the constant,
K, associated with transport capacity
Figure 90,
11 22 33
AVERAGE RUNOFF DEPTH (CM) X 10
44
55
Sensitivity of sediment load to runoff depth
160
-------
ADSORPTION - DESORPTION SUBMODEL (ADDE) AND SENSITIVITY ANALYSIS
Adsorption and desorption are the controlling processes
in the dispersal of pesticide in the soil. Pesticide dispersal
is dependent on the chemical properties of the pesticide, the
physical characteristics of the soil, the meteorological conditions,
and the type and stage of development of the plant cover. Once
the chemical properties have been understood and related to the
physical soil properties in the adsorption processes, pesticide
movement can then be predicted within the range of meteorological
events common to the watershed.
Various scientists have studied this problem. Numerous
experiments aimed at understanding portions of the adsorption-
desorption process have been carried out. Rifai, Kaufman and
42 43 44
Todd, Day and Forsythe, Nielsen and Biggar, and Rose and
45
Passioura, studied steady state displacement of water satura-
ted porous material and solutes which do not interact with the
46
solid soil matrix. Likewise, Biggar and Nielsen, Kay and
Elrick, and Huggenberger, Letey and Farmer experimented
with solutes that are highly adsorbed onto the solid soil matrix.
These studies do not address the simultaneous movement of water
and solutes that occur naturally.
A modified adsorption-desorption hybrid model developed
49
by Dr. J. M. Davidson was used in the simulation of pesticide
adsorption-desorption. Dr. Davidson's model addresses the
"combined effect of convection, adsorption, and dispersion" with
a correction for numerical dispersion. Modifications were
made to adapt the existing model for use within the simulation
structure.
161
-------
Description of the Adsorption-Desorption Model
The one dimensional transport of solute through soil is
described by:
= 3_ D 9C _ 3(gC) _ 9S_
D P
_ _
TE 8Z 3Z 8Z 9t
where C = solute concentration (yg/cc )
8 = volumetric water content (cc/cc)
t = time (hr)
Z = depth into the soil measured positive in a down-
ward direction (cm)
2
D = apparent diffusion coefficient (cm /hr)
q = volumetric flux of water (cm/hr)
p = soil bulk density (g/cc)
S = adsorbed solute concentration (ug/g)
The adsorption and desorption processes of Equation (47) are
described by the Freundlich equations:
S = KAC1//N adsorption (48)
S = KDC1//AB desorption (49)
where K = adsorption distribution coefficient
A
K = desorption distribution coefficient
N = adsorption exponent constant
AB = desorption exponent constant
Assuming that D is independent of soil depth, and follow-
49
ing the Davidson methodology, Equation (47) reduces to:
162
-------
D
3t
3Z
where W = 1 +
2 W
W = 1 + - K C
9AB D
3Z 9 | 3Z
,1/N-l
1/AB-l
adsorption
desorption
(50)
(51)
(52)
The equations are solved explicitly using a finite difference
procedure corrected for numerical dispersion described by
Chaudhari (1971). 50
j =
At
. ,
(AZ)
+ C1""1
^
At
1
AZ
j _ At j, /rj-l
i 2~ Gi} (Ci
(53]
where
i^ = 1/2 XV AZ - (X^;
At-G^
(54)
2AZ
(55)
(ej)2
j 2
- 2(D3)2
- 2Q-! + Q-!
2AZ
2AZ
AZ'
-1 '-2 (°
jJ+ - qJ
i\ 2A~Z
AZ'
)-^ At
(56)
163
-------
and j = time index
i = spatial depth index
t = time increment
Z = spatial increment
There are a few restrictions in the use of the adsorption-desorp-
tion hybrid model. The time increment, At, must always satisfy
the following criteria:
(D-Dn) At
and
[f - f H] " 5 V4
<58>
When associated with an infiltration model that moves water
through the soil, the spatial increment, AZ, in the solute
equation must be an integer multiple of the spatial increment
in the water transport equation. This interaction of spatial
increments, AZ, and time increments, At, restricts the results
to compatible water models. This restriction is not significant
in the SCRAM simulation structure because of the small time
increments used in the simulation.
Sensitivity Analyses
Davidson's adsorption-desorption model was tested to
determine its sensitivity to variations in the input parameters.
Parameters were tested over two time regimes (one hour and five
hours of continuous infiltration) . The model was most sensitive
to layer thickness. Variation of exponent constants, the
diffusion coefficient and the conductivity of the pesticide have
little effect on the model results.
164
-------
Sensitivity to layer thickness -
To test the sensitivity of the adsorption-desorption
model to variations in soil layer thickness, two soil layer
thicknesses, 0.5 cm and 1 cm, were compared for two time periods
of continuous infiltration: one hour and five hours. The soil
layer thickness affects the depth of pesticide penetration into
the soil profile and determines the depth of the maximum-
pesticide concentration in solution and adsorbed on the soil.
The solute portion of the pesticide concentration
penetrates deeper into the soil profile and the maximum pesticide
concentration occurs at a deeper soil depth with the larger soil
layer thickness (1 cm) for both the 1 hour and 5 hour time per-
iods. (See Figure 91). The difference in layer thickness
dependence of the depth of the maximum pesticide concentration
is reduced within five hours.
The adsorbed portion penetrates deeper into the soil
profile and a larger portion of the pesticide is adsorbed with
1 cm soil layer thickness. Figure 92 shows the concentrations
of the adsorbed pesticide. The relationships between soil layer
thickness and adsorbed pesticides exist for both the one hour and
five hour time period.
Sensitivity to the adsorption exponent constant -
N is the exponent constant in the Freundlich adsorption
equation. Its value is dependent on the pesticide being modelled,
Varying N from 1.0 to 9 (the values used for diphenamid and
atrazine) produces negligible change in the 1 hour and 5 hour
graphs of both the chemical concentration in solution and
adsorbed to the soil as seen in Figures 93 and 94.
165
-------
6.5
3 5.2
z
o
DC
I-
Z
LU
O
Z
o
o
LU
I-
_J
O
10
LU
Q
y 1.3
3,9
2.6
H = 0.50 t = 1
H = 0.50 t = 5
H = 1.0 t = 1
H = 1.0 t = 5
SOIL DEPTH (cm)
Figure 91.
Layer thickness vs solution concentration
distribution
o
t/3
o
<
LU
O
Z
o
o
Q
LU
CO
DC
O
C/3
Q
LU
Q
o
H
LU
Q.
H = 0.50 t = 1
H = 0.50 t = 5
H =• 1.0 t » 5
H « 1,0 t - 1
10 15
SOIL DEPTH (cm)
20
25
Figure 92
Layer thickness vs adsorbed concentration
distribution
166
-------
N = 1.0 t = 1
N = 0.9 t = 1
N=1.0t =
= 0.9 t = 5
10 15
SOIL DEPTH (CM)
20
25
Figure 93,
Adsorption exponent vs. solution concentration
distribution
o
03
2
o
oc
H
Z
ui
O
2
O
U
Q
HI
CD
X
O
s
<
tu
Q
o
CO
LU
Q.
10 15
SOIL DEPTH (CM)
25
Figure 94
Adsorption exponent vs. adsorbed concentration
distribution
167
-------
Sensitivity to the desorption exponent constant -
AB is an exponent constant associated with desorption
in the Freundlich equation. The value assigned to AB is pesticide
dependent. AB was varied over a range from 2.5 to 1.7 (the
values used for*diphenamid and atrazine). As seen in Figures 95
and 96, the adsorbed and solute concentrations show negligible
dependence on the value assigned to AB.
Sensitivity to the diffusion coefficient -
D is the apparent diffusion coefficient in the pesticide
transport equation. Varying D from .05 to 0.5 does not
significantly affect the solution concentration distribution or
the adsorbed concentration distribution as seen in Figures 97
and 98.
DEGRADATION SUBMODEL (DEGRAD) AND SENSITIVITY ANALYSES
Degradation of pesticides in the soil is a complex
phenomena involving a variety of mechanisms. Among the known
mechanisms are chemical, photochemical, and microbial degrada-
tion. The quantification of these mechanisms and the effects
of environmental factors on the degradation rates of pesticides
remains an area of active research.
Most research on degradation has explored the subject
under laboratory conditions. These studies have held environ-
mental conditions constant in order to examine specific
mechanisms.
168
-------
AB = 1.7 t = 1
10 15
SOIL DEPTH (cm)
20
Figure 95
Desorption exponent vs. solution concentration
distribution
_ 8.0 r
10 15
SOIL DEPTH (cm)
20
25
Figure 96
Desorption exponent vs adsorbed concentration
distribution
169
-------
6.5 r
10 15
SOIL DEPTH (cm)
t = 1
D = 0.05
D = 0.50
20
Figure 97
Diffusion coefficient vs. solution concentration
distribution
10 15
SOIL DEPTH (cm)
20
25
Figure 98.
Diffusion coefficient vs. adsorbed concentration
distribution
170
-------
Moe investigated the kinetics of raicrobial degradation of
the herbicides IPC and CIPC. From an equation based on both
the herbicide concentration and the bacterial mass present in
the system, the reaction rate constants for the initial hydrolysis
reactions were calculated. Moe determined that the greater
persistence of the herbicide CIPC was more dependent upon the
degree of microbial activity rather than upon an activation energy
requirement.
52
Burschel and Freed studied the rate of micro-biological
decomposition of three organic herbicides in the soil. To
ascertain the kinetics involved in the process, the rate was
followed at two different temperatures. They reasoned from both
first principles and observations that since most microbiological
processes follow first order kinetics, then the decomposition of
the herbicides in the soil should also follow first order kinetics.
On this basis it would be possible to calculate the heat of
activation required for this breakdown, by applying the Arrhenius
equation. The data they presented supported this conclusion.
Schultz and Tweedy investigated the uptake and metabolism
of diphenamid in resistant (tomato) and susceptible (wheat) plants.
They proposed a degradation scheme for diphenamid in plants, and
examined the toxicity of the herbicide and its metabolism in
tomato and wheat plants.
Freed determined that as a first approximation, degrada-
tion of the herbicides examined followed a first order rate law.
Several mathmatical models have been proposed to describe
the degradation process: first order kinetics, Michaelis-Menton
kinetics, half-order kinetics, and more complex schemes.
Steen55 (from SERL/EPA) has developed a first order model
including soil moisture and temperature factors:
171
-------
K(M,T)CP (59>
The equation is solved:
Cp = [Cp]Qe~K(M,T)t (60)
The model has been tested using a combination of laboratory and
field data. The herbicide used to calibrate the model was
diphenamid.
The temperature and moisture dependence of degradation
is expressed:
x
T -T
max (t)
T -T
max opt
BK(Tmax-Topt)
(61)
Parameters AK and BK are herbicide dependent. Parameter AK is
determined by the relationship of soil moisture levels to the
herbicide decay rate.
AK assumes soil moisture has a Gaussian distribution
with time. BK is an empirical fitting parameter which includes
the effects of biological components of degradation. Environ-
mental parameters include: T,M,K ., T ., M ., and T . K .
' ' opt' opt' opt' max opt
is the decay rate at the optimal temperature. T is the
optimal temperature expressed in degrees C. T is the maximum
in 3.x
temperature and M is the optimal moisture level.
172
-------
The boundary conditions in the model involve the
temperature. An increased temperature increases the rate of
degradation. As the temperature approaches 40°C, the rate of bio-
logical degradation decreases. At higher temperatures (between
42° - 45°C) degradation ceases.
Experimental Degradation Data
Herbicide data was collected on the watersheds and
attenuation plots. Pesticide loss was plotted against elapsed
time in days of the watersheds. Both atrazine and diphenamid
appear to exhibit a first-order decay (Figures 99 thru 102).
Paraquat core sample data (Figures 103 thru 106) does not show a
consistent decay pattern with time. Paraquat levels within a
watershed will inexplicably increase over a period of time after
dropping to a lower level. Paraquat data was not simulated using
the herbicide degradation model because of fluctuations in the
degradation of the core sample data.
Averaged data for diphenamid (Figure 107) and paraquat
(Figure 108) from attenuation plots in 1972 have been plotted
against time. The two pesticides show erratic fluctuations.
An improved coring technique was devised to prevent contamination
of subsurface soil. This technique has provided remarkably
improved data for 1973.
Diphenamid core data from watershed P-01 and atrazine
core data from watershed P-04 were compared to simulated results
using only the degradation model (Figures 109 and 110). The
environmental parameters used in this simulation were optimal
moisture and a 20°C temperature.
173
-------
110
32 48
ELAPSED TIME (DAYS)
64
80
Figure 99.
110
oc
88
1 66
44
22
Figure 100,
P-01 watershed: percent of applied diphenamid
remaining during the 1973 growing season based
on averaged core sample data
14
28 42
ELAPSED TIME (DAYS)
56
70
P-02 watershed: percent of applied atrazine
remaining during the 1973 growing season based
on averaged core sample data
174
-------
110
16
32 48
ELAPSED TIME (DAYS)
64
Ficrure 101..
110 ,.
cc
H
a* 44 •
22 -
P-03 watershed: percent of applied diphenamid
remaining during the 1973 growing season based
on averaged core sample data
14
28 42
ELAPSED TIME (DAYS)
56
Figure 102
P-04 watershed: percent of applied atrazine
remaining during the 1973 growing season
based on averaged core sample data
175
-------
110
16
32 48
ELAPSED TIME (DAYS)
64
80
Figure 103,
tr
<
0.
110
88
66
44
22
P-01 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data
14
28 42
ELAPSED TIME (DAYS)
56
70
Figure 104
P-02 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data
176
-------
110
88
a
<
a:
<
a.
66
44
22
16
32 48
ELAPSED TIME (DAYS)
64
80
Figure 105
110
88
a ee
<
DC
<
a.
44
22
Figure 106
P-03 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data
14
28 42
ELAPSED TIME (DAYS)
56
70
P-04 watershed: percent of applied paraquat
remaining during the 1973 growing season based
on averaged core sample data
177
-------
110
88
66
LU
I
o. 44
Q
22
12
24 36
ELAPSED TIME (DAYS)
48
60
Figure 107.
110
88
O
<
IT
<
o.
66
44
22
Percent of applied diphenamid remaining on
attenuation plots during the 1972 growing
season averaged over all samples
12
24 36
ELAPSED TIME (DAYS)
48
60
Figure 108.
Percent of applied paraquat remaining
on attenuation plots during the 1972
growing season averaged over all samples
178
-------
3660 r
\-« MODEL (17.5% MOISTURE; 20°C)
N
34 51
ELAPSED TIME (DAYS)
68
85
Figure 109
4510
Watershed P01: comparison of simulated versus
actual diphenamid degradation
£ 3608
a.
Z
O
1-
Hn 2706
Z
111
o
Z
o
o
u] 1804
Z
N
<
CE
< 902
0
_\
\
_\
. \\
\\
\\
\\
\\
\ \
\\
\ ^
\ \
\ V« MODEL (17.5% MOISTURE; 20°C)
X N
\ \
- \ xx
*"-
DATA ^^ "" *" "• -
, " — : 7 T
14
28 42
ELAPSED TIME (DAYS)
56
70
Figure 110.
Watershed P04: comparison of simulated versus
actual atrazine degradation
179
-------
Degradation Sensitivity Tests
Sensitivity tests for the degradation model were per-
formed for two distinct groups of model parameters: (1)
environmental parameters, and (2) pesticide specific parameters.
All tests were run for a period of 80 days. All the parameters
examined in the sensitivity analysis are factors which determine
the decay constant K, , (Equation 61).
Sensitivity to environmental parameters -
The environmental parameters tested were moisture and
temperature. For these tests the pesticide specific parameters
AK, BK were assigned diphenamid values.
The parameters used to calculate K(M,T) were assigned
the following values :
Kopt = *119676 Tmax= 39'6065
M . = .173599 AK = 92.0040
opt
T = 38.2344 BK = 0327710
opt
Moisture was found to be the more sensitive environmental
parameter. Moisture sensitivity of the degradation model
was tested over the range of 0% to 35% moisture. Figures
111 through 115 illustrate the effects on degradation of 0% of
moisture, 35% moisture, and 17.5% moisture. These three moisture
levels were plotted for five temperatures: 0°C, 10°C, 20°C,
30°C, and 35°C. Both maximal and minimal moisture produce minimal
degradation at all temperatures examined. A moisture level of
17.5% produces near optimal degradation at all temperatures
examined. The effect of moisture on degradation was graphed
over a range from 5 percent to 30 percent moisture in 5
percent increments (Figure 116) . The extremes of this range, 5
180
-------
2900
18
0.0% MOISTURE
36 54
ELAPSED TIME (DAYS)
72
90
Figure 111.
Sensitivity of the degradation model to moisture
at 0°C
2900
- 2320
CD
o.
a.
Z
o
< 1740
DC
O
z
8 1160
Q
580
18
0.0% MOISTURE
36 54
ELAPSED TIME (DAYS)
90
Figure 112.
Sensitivity of the degradation model to moisture
at 10°C
181
-------
2900
0.0% MOISTURE
18
36 54
ELAPSED TIME (DAYS)
72
Figure 113,
Sensitivity of the degradation model to
moisture at 20°C
2900
Figure 114,
0.0% MOISTURE
18
36 54
ELAPSED TIME (DAYS)
72
Sensitivity of the degradation model to
moisture at 30°C
182
-------
2900
18
0% MOISTURE
36 54
ELAPSED TIME (DAYS)
72
90
Figure 115.
Sensitivity of the degradation model to
moisture at 30°C
2900 r
. 5% MOISTURE
25% MOISTURE
10% MOISTURE
ELAPSED TIME (DAYS)
Figure 116.
Sensitivity of the degradation model to
moisture at 20°C
183
-------
percent and 30 percent, produce relatively low degradation.
Moisture levels of 10 and 25 percent produce moderate levels of
pesticide degradation, while moisture levels of 15 and 20 percent
product rapid degradation.
Temperature variations, while not producing the dramatic
affect of moisture variation, produce significant effects.
Temperature sensitivity was tested over a range from 0-35 degrees.
Degradation produced at the temperatures examined (0°, 10°, 20°,
30°, and 35°) were plotted at minimal, optimal and maximal
moisture levels (Figures 117 thru 119). Degradation of pesticide
increases with temperatures up to 38°C. At 40°C, the degradation
rate is analagous to the degradation produced at 15°C. Biological
degradation ceases at temperatures between 40°C and 45°C. The
computer model currently uses 42°C pending the completion of
Dr. Steen's tests.
Sensitivity to Pesticide Specific Parameters
The environmental parameters were assigned the following
values for all pesticide specific sensitivity tests:
K , = 119676 T = 39.6065
opt max
M = .173599 T = 20.000
T = 38.2344 M = .175000 for tests of BK
= .05000 for tests of AK
AK characterizes the moisture dependence of pesticide
degradation. The value of AK is always negative; a value of
zero would produce optimal degradation at all moisture levels.
The effect of increasing the absolute value of AK is the
reduction of the degradation rate. AK was varied from 75
to -110 with little effect on the degradation rate (Figure 120).
184
-------
2900 ,.
_; 2320
CD
o.
IX
o
DC
H-
LU
O
2
O
O
Q
01
I
a.
Q
1740
1160
580
18
36 54
ELAPSED TIME (DAYS)
72
30°C
35°C
90
Figure 117,
Sensitivity of the degradation model to
temperature at minimal moisture (0%)
2900
18
36 54
ELAPSED TIME (DAYS)
72
90
Figure 118,
Sensitivity of the degradation model to
temperature at optimal moisture (17.5%)
185
-------
O
U
Q
LU
I
2900
2320
m
o_
Q.
O 1740
CC
I-
Z
LU
1160
580
18
36 54
ELAPSED TIME (DAYS)
72
30°C
35°C
90
Figure 119
Sensitivity of the degradation model to
temperature at maximum moisture (35%)
BK characterizes the temperature dependence of pesticide
degradation. As BK increases from 0.01 to 0.05 the rate of
pesticide degradation decreases (Figure 121). At BK = 0, the
rate of degradation is independent of temperature. Values of
BK greater than 0.5 result in little or no degradation.
VOLATILIZATION SUBMODELS (VOLT) AND SENSITIVITY ANALYSES
The volatilization of pesticides is one of the mechanisms
for the removal of the pesticide from the soil to the
atmosphere. Among others, Dr. Walter J. Farmer studied this
process in an attempt to develop models for predicting the loss
of pesticides from the soil due to volatilization.
186
-------
2900
° 2320
Z
O
cc
I- 1740
LU
O
z
o
g 1160
O
H
UJ
CL
580 .
36 54
ELAPSED TIME (DAYS)
72
90
Figure 120,
2900
Sensitivity of the degradation model to the
pesticide specific parameter-AK
18
36 54
ELAPSED TIME (DAYS)
90
Figure 121,
Sensitivity of the degradation model to the
pesticide specific parameter-BK
187
-------
His recent paper contained five models describing the volatili-
zation of pesticides with varying initial and boundary conditions
and transport processes.
Farmer notes that the volatilization of pesticides can be
predicted by studying the physical and chemical processes
which control the pesticide concentrations at the soil surface.
When pesticide concentration at the surface is high, volatiliza-
tion is primarily governed by the pesticide vapor pressure and
degrees of adsorption in the soil. When concentrations at the
surface are lower, however, volatilization is governed by the
movement of the pesticides through the soil to the surface. The
pesticide transport can be by either one or both of the possible
transport processes of mass flow and diffusion.
The five Farmer models are more accurately designated as
distinct solutions to a single equation. The basic assumption is
that the movement of the pesticide in soils under concentration
gradient can be mathematically treated using the standard
equation. The change in concentration of the pesticide, as well
as the loss of pesticides due to volatilization at the surface,
is predicted by the solution of the diffusion equation using five
different sets of boundary conditions. Because of the similarity
of (1) the diffusion equation and the transfer of matter into a
concentration gradient described by Pick's second law and (2)
the heat transfer equation described by Fourier's law, it is
possible to use known solutions of the heat transfer equation to
describe pesticide movement. If the soil is assumed to be an
isotropic system, wherein a pesticide is uniformly mixed with a
layer of soil and is volatilized at the soil surface, the
diffusion equation is:
188
-------
3 C 1 8C
—o - ~ -^r = 0: Pick's Second Law (62)
9z2 D 3t
where: C = the pesticide concentrated in the soil
(g/cm total volume)
z = distance measured normal to the soil surface (cm)
D = diffusion o
t = time (sec).
2
D = diffusion coefficient (cm /sec)
The solution of this equation with the five sets of boundary con-
ditions has been described by Farmer. The actual closed form
solutions are obtained through comparison to similar heat transfer
r O
situations described in H.S. Carslaw and J.C. Jaeger.
Portions of Farmer's paper are duplicated and discussed below
for the convenience of the user.
Model I
The first model assumes that the pesticide volatilizes
at the soil surface. Pesticide is initially incorporated uniformly
to a depth L at concentration C (g/cm ). No pesticide diffuses
below L. Mathematically these conditions are:
C = C at t = 0; 0 0
SP
= 0 at Z = L
The solution to (62) by analogy to the heat equation is: (63)
189
-------
r - ~ V (-1)" L (~D(2n + J-)2^ t/4L2)
C_ — > / rt r^i \"~ i ti
""Q TT>
n=o
x cos
(2n + 1) TT (L-Z)
2L
(64)
Pesticide flux, f = D (-5—) is given by
^z/z = 0
D C
f =
(TTDt)
1/2
1 + 2
I
n=l
(-n2 L2/Dt)
(65)
Model II
If the summation term in (65) is small compared to one,
the flux reduces to:
D C
f =
(TTDt)
1/2
(66)
By analogy to heat flow in an infinite solid, the concentration
is given by:
C = CQ erf [z/2 (Dt)1//2J
(67)
A test for the validity of (66) suggested by Farmer is
C(z = L, t) > 0.99C . For this to be true it is easy to show
— o
that:
t < IT/14.4 D
190
-------
For L = 1 cm and D = 8.64 x 10 cm2/day, Equation (66) is valid
20
for 8 days. Bode et al reported diffusion coefficients
_ Q _ £T O
for trifluralin between 10 and 10 cm /sec. Under conditions
which are likely to occur in the field, values larger than 10~
2 -32
cm /sec (8.64 x 10 cm /day) are unlikely. Accordingly, Equation
(66) would be valid for 8 days for L = 1 cm and for 200 days
L = 5 cm.
Advanced Models
The remaining models discussed by Farmer attempt to
account for the weakness of the assumed boundary conditions
(Equation 63) .
Farmer's Model III addresses the fact that diffusion
can occur across the lower boundary, i.e.,:
(68)
For reasons which will be discussed in detail below, this is
not a significant error because of a more fundamental problem.
The remaining two models discussed by Farmer deal with
the assumption that the pesticide concentration at the soil sur-
face is zero at t > 0. Both models have the effect of reducing
the pesticide flux at the surface.
Fundamental to all of the Farmer models is the assumption
that the pesticide is uniformly incorporated to a depth L. As
can be seen from Figure 122 (smoothed data) the pesticide is
far from uniformly incorporated. Hence the derivation of the
equations which are the basis for all of Farmer's models is
highly questionable. Accordingly, at this time we have only
coded the simpler models hereinafter referred to as Model I and
Model II.
191
-------
2400
2 -
— 4 -
a.
UJ
a
o
in
ATTENUATION PLOTS
P-01 WATERSHED
P-03 WATERSHED
1 0
400
800 1200 1600
CONCENTRATION OF TRIFLURALIN (PPB)
2000
2400
Figure 122.
Measured Trifluralin distribution in the
soil profile after application, 1973
192
-------
Adjustment for Non-Uniform Pesticide Application
In order to adjust for the lack of uniformity of
pesticide in the soil profile, Equations (65) and (66) were
applied in the following manner:
1. L was defined to be one centimeter and C was set
o
equal to C,, the concentration in the 0-1 cm layer.
2. When C, was reduced to the concentration in the 1-2 cm
layer, C was set equal to C~ and L was set to 2.0 cm.
3. This process was continued until the concentration in
the soil profile reached the concentration in the
lowest centimeter.
The effect of this modification can be seen in Figures 123 and
124. For comparison Model II has been plotted for two different
3 3 — 7
values of C : 5000 ng/cm and 1400 ng/cm . A value of 1.0 x 10
2 °
cm /sec was used for the diffusion coefficient.
Diffusion Coefficient for Trifluralin
A number of experiments have shown that the diffusion
coefficient D is a function of the soil moisture content, soil
temperature, and soil bulk density.
Bode used a multiple regression analysis to derive a
15 term equation for predicting the diffusion coefficient from
trifluralin:
193
-------
300
MODEL II
MODEL II MOD 1
C = C = 1400 ng/cm
O 9
20
40 60
ELAPSED TIME (DAYS)
100
Figure 123
a
z
HI
tr
LJ
Q
O
I-
U5
UJ
100
80 —
60
40
20
Figure 124
Calculated pesticide flux for different initia]
conditions
MODEL
— — — — — MODELII,MOD1
20
40 60
ELAPSED TIME (DAYS)
80
100
Pesticide remaining for different initial
conditions
194
-------
log D = - 0.313 - 1.051 6 + 0.054(0)2
- 8.494 x 10~463 - 8.997 p
+ 6.021 x 10~59T2 - 7.359 x 10~70T3
+ 1.483 x 10"664T - 8.863 x 10~805T
+ 1.362 x 10~966T + 1.5880p
- 0.10802p + 2.880 x 10~303p
- 2.560 x 10~504p + 4.664 x 10~2Tp
- 3.013 x 10~30Tp (69)
where 6 = soil moisture (% w/w)
T = soil temperature (°C)
p = bulk density (g/cm )
The multiple correlation coefficient (R) for Equation (69) was
0.99, which is very satisfactory -
Equation (69) was derived from experimental results
for Mexico Silt Loam of varying bulk densities. The soil was
reported to be 2.5% organic matter, 75% silt, and 22% clay
and had a pH of 5.6.20
Equation (69) predicts that D will decrease with increasing
bulk density for constant temperature and moisture. For constant
moisture and bulk density, D increases as temperature increases.
For constant bulk density and temperature, D increases and then
decreases as moisture content is varied between 0 and 30%(see
Figures 125 and 126).
From Figures 125 and 126 and Equation (69) we can see that
the diffusion coefficient drops off rapidly as the moisture
content goes below 5% regardless of the soil temperature, and
when the soil temperature drops below 25°C there is very little
change in D regardless of the moisture content.
195
-------
T = 49
10 15 20 25 30
SOIL MOISTURE (% W/W)
Figure 125.
Calculated trifluralin diffusion coefficient for
Mexico Silt Loam (Bulk density 1.4 g/cc)
21 .a
(N
'o
14.0
10-5
LU
O
u
z
o
7.0
t 3.5
Q
10 15 20 25
SOIL MOISTURE (% W/W)
30
Figure 126.
Calculated trifluralin diffusion coefficient for
Mexico Silt Loam (Bulk density 1.0 g/cc)
196
-------
Assuming that high soil temperatures will not be associ-
ated with high moisture content, a range of values for D can be
estimated for field conditions. For Mexico Silt Loam of bulk
density 1.4 g/cm , this range would be approximately 9 x 10
cm2/day (moist, 25°C) to 5 x 102 cm2/day (dry, 45°C).
Model Sensitivity to the Diffusion Coefficient and Soil Profile
Distribution
In order to test the model sensitivity to the diffusion
coefficient D, an initial distribution of pesticide in the soil
profile must be assumed. Unless otherwise noted, the application
2
amount is assumed to be 11,220 ng/cm , distributed in the first
eight centimeters as follows:
45%, 28%, 14%, 6%, 3%, 2%, 1%, 1%.
The flux predicted by Model II increases as the square
root of the diffusion coefficient. As D increases the non-uniform
incorporation of pesticide becomes more significant. For large
values of D the flux at the surface due to the high concentration
of pesticide in the 0-1 centimeter layer will be very large. As
a result the concentration in the 0-1 cm layer is reduced very
rapidly to the concentration in the 1-2 layer (less than two days
for the conditions outlined above). These results suggest that
between 5 and 10% of the total amount applied could be lost in
the first 4-8 hours after application. The sensitivity to D is
shown in Figure 127.
Because of the possible sensitivity to the initial pro-
file of pesticide in the soil, a series of computer runs were
-2 2
made with D held constant at 8.64 x 10 cm /day, and the total
2
application fixed at 11,220 ng/cm . If we represent the profile
concentrations as percent of amount applied, in vector notation
197
-------
three profiles were checked: A: (73.5, 13, 6, 3, 1.5, 1,1,1),
B: (45, 28, 14, 6, 3, 2, 1, 1), and C: (12.5, 12.5, 12.5, 12.5,
12.5, 12.5, 12.5, 12.5).
The results are shown in Figure 128. Table 14 summarizes
the results for several values of the diffusion coefficient. The
effect of initial pesticide distribution is very pronounced. For
reasonable values of the diffusion coefficient significant amounts
of pesticide would be lost in the first few days after application,
Table 14. PERCENT PESTICIDE REMAINING AFTER 100 DAYS AS A
FUNCTION OF INITIAL DISTRIBUTION AND DIFFUSION
COEFFICIENT
Percent Remaining After 100 Days
2
Diffusion Coefficient (cm day)
Pesticide __ „
Distribution 8.64 x 10 8.64 x 10 8.64 x 10
A 36.4 15.9 2.4.9
B 64.0 29.2 6.0
C 86.9 58.5 0
Diffusion in the Soil Profile
None of the models discussed above predict any downward
(away from the surface) diffusion of pesticide. This result
would be expected for uniform incorporation but not for the non-
uniform case. To correct for this effect another modification
was added to Model II (Mod 2).
We assumed that the pesticide would move according to
the concentration gradient, i.e., Pick's first law:
198
-------
100
= 8.64 X 10"3cm2/DAY
8.64 X 10"2cm2/DAY
D = 8.64 X 10~1 cm /DAY
Figure 127.
100
40 60
ELAPSED TIME (DAYS)
100
Sensitivity of Model II (Mod 1) to the diffusion
coefficient (D)
[12.5. 12.5, 12.5, 12.5
145,28,14,6,3.. ]=PROFILE%
20
40 60
ELAPSED TIME (DAYS)
100
Figure 128.
Sensitivity of Model II (Mod 1) to pesticide
distribution in the soil profile
(D = 8.64 x 10-2 cm-2/day)
199
-------
(70)
The diffusion coefficient may be specified as constant throughout
the profile, or, using the equation by Bode, calculated from
the moisture content, temperature, and bulk density.
The effects of this modification are shown in Figures
129 and 130 for two different values of the diffusion coefficient.
The pesticide distribution was (60, 20, 10, 4, 2, 2, 1, 1, ) for
-2 2
both cases. For values of D j> 8.64 x 10 cm /day the model
predicts significant interlayer diffusion. The total pesticide
loss is also changed but not significantly.
EVAPOTRANSPIRATION SUBMODEL (EVAP) AND SENSITIVITY ANALYSES
Moisture transfer from a vegetated surface through the
mechanism of evaporation is termed evapotranspiration. The word
combines the two similar but distinct processes of evaporation and
transpiration. Evaporation is the process whereby liquid water
passes directly into the vapor state, while transpiration is the
process whereby water passes from liquid to vapor via plant
metabolism. The two processes are usually combined due to the
fact that they are indistinguishable from one another in
experimental measurements.
The net transfer of water molecules into the air as
evaporation is a function of the vapor pressure gradient between
the evaporating surface and the air. The gradient implies that
the vapor pressure of the air adjacent to the surface is less
than that at saturation. The change of state from liquid to vapor
requires energy, about 582 calories per gram of water at 25°C,
which necessitates an external source of energy. This could be
solar radiation or sensible heat from the atmosphere on the
200
-------
3000
2800
2600 ~
200
Figure 129.
10 20 30 40 50 60 70 80 90 100
ELAPSED TIME (DAYS)
Trifluralin soil profile concentration Predicted
by Model II (Mod 2) for D=8.64 x 10~3 cm2/day
201
-------
m
a.
Q.
g
i-
LU
O
O
O
DC
D
LL
E
3000
2800
2600
2400
2200 -
2000
1800
1600 -
1400
1200 —
1000 -
800 ~
600
400 -
200
10
100
ELAPSED TIME (DAYS)
Figure 130,
Trifluralin soil profile concentration predicted
by Model II (Mod 2) for D=8.64 x 10~2 cm2/day
202
-------
ground. Alternatively, the energy may be drawn from the kinetic
energy of water molecules, thus cooling the water until equili-
brium with the atmosphere is established and evaporation ceases.
In general, however, solar radiation is the principal energy
source for evaporation.
The major controlling factors for evaporation are vapor
pressure deficit and available energy, although wind speed,
temperature of the evaporating surface, and purity of the water
also affect the occurrence and rate of evaporation. Wind speed
enables new parcels of unsaturated air to move over the evapora-
ting surface. At higher surface temperatures more molecules of
water can leave the surface due to their greater kinetic energy.
The purity of the water affects the energy of vaporization re-
quired per unit weight of water. Salt for example, depresses
the rate of evaporation about 3% in concentrations common to sea
water.
Transpiration, the water loss from plants, is also a
function of a vapor pressure gradient between the pressure
of the air and that in the leaf cells. About 90% of the diurnal
water loss occurs during daylight, because the water vapor is
transpired through small pores (stomata) in the leaves which
open in response to stimulation by light. Transpiration performs
a vital function in the plant by affecting the internal transport
of nutrients and the cooling of leaf surfaces. A complicating
factor affecting transpiration is the interaction between soil
moisture content and root development. If soil water is not
replenished over a period of weeks, vegetation with deeper roots
will transpire more than shallow rooted plants, other factors
being equal.
203
-------
When the moisture supply in the soil is limited, the factors
cited above as controlling evaporation and transpiration are not
as important, and the movement of the water through the soil is
the controlling factor. In this event, the actual rate of
evapotranspiration falls short of what is termed potential
ev.apotranspiration, the rate of evapotranspiration which would
occur if the supply of water to both the plants and the evapora-
ting surface was unlimited. Analytical approaches compute only
the potential evapotranspiration.
The relationship between these two terms - potential and
actual - is a controversial one. At field capacity, which means
maximum soil moisture content with free drainage, the ratio of
actual to potential transpiration proceeds at the maximum
potential rate. One view is that this potential rate is main-
tained until soil moisture content drops below some critical
value, after which there is a sharp decrease in evapotranspira-
tion. An alternate view maintains, however, that the rate
decreases progressively with diminishing soil moisture. Recent
experimental work has indicated that both views may be accurate
for varying soil types and climatic conditions. The former
applies in general to heavy soils in a relatively humid region,
while the latter applies to sandy soils in arid regions.
There are two analytical approaches for computing potential
evapotranspiration. The first approach is based upon aero-
dynamic principles and evaporation is regarded as due to
turbulent transport of vapor by eddy diffusion. The second
approach is based upon energy conservation and evaporation is re-
garded as one of the ways of degrading incoming radiation.
Mathematically the aerodynamic approach is expressed as:
E = f(y) (e - e) (71)
204
-------
where E = evaporation
y~ = mean wind speed at height 2
e = saturation vapor pressure
o
e2 = vapor pressure at reference height 2.
Equation (71) relates evaporation from large surfaces to the mean
wind speed and the vapor pressure difference between the evapora-
ting surface and the reference height 2. The function f (vu) has
been postulated in simple form depending only on y?, and in
complex forms which account for wind speed and turbulence.
The alternate approach is an energy balance about the
evaporating surface. From fundamental principles of the conser-
vation of energy, it follows that the net total of long and short
wave radiation received at the surface is available for three
processes. These three are the transfer of sensible heat to the
atmosphere, the transfer of latent heat to the atmosphere (this
energy is equal to the product of the latent heat of vaporization
and the amount of evaporization), and the transfer of sensible
heat into the ground. If the other variables can be determined,
then the evaporization can be computed algebraically.
A number of methods have been developed to combine the
aerodynamic and energy budget approaches, thereby eliminating
certain measurement difficulties which each presents in an effort
to obtain input parameters. This so called combination approach
was suggested by H. L. Penman in 1948 and has been the major
technique utilized since that time. The actual Penman formula-
tion has been modified more recently to include a term describing
the stomata resistance as well as to correct some empiricism
64
used by Penman in his original approach. C. H. M. Van Bavel
offered both changes to the Penman formulation as well as experi-
64
mental verification of his own formulation in 1966. Van Bavel's
combination approach has been widely used since that time.
205
-------
Three major assumptions are made when using a combination
approach to compute potential evapotranspiration: (1) the
assumption that the vertical divergence of the fluxes between
surfaces and point of measurement, z is negligible, (2) the
assumption that the turbulent transfer coefficients for water
vapor and sensible heat are substantially equal and (3) the
assumption that the value of A/y, (de /y dT) can be taken at the
o
temperature T rather than at the average of the unknown
Z
surface temperature T and the elevated air temperature T .
s z
The evapotranspiration model used in the simulation
structure utilizes the Penman combination approach with the Van
Bavel modifications. Evapotranspiration is computed as:
E =
f
H 4-
?
A+ -
y
p C d '
o. fcj Q.
Y Ta
L + ^
T,
where
E
w
L
A
Y
H
potential evapotranspiration (cm/sec)
density of water (g/cm )
latent heat of vaporization (cal/g)
slope of the saturation vapor pressure versus
temperature curve (mb/°C)
psychrometric constant (mb/°C)
net sum of radiative flux, soil heat flux,
heat storage changes in vegetation or ponded
water and photosynthetically used energy not
including the latent heat (LE) and the sensible
heat.
206
-------
p = density of moist air
3.
C = specific heat at constant pressure of air
d = vapor pressure deficit - the difference between
a
the saturation vapor pressure at a given temper-
ature and the actual vapor pressure
T = atmospheric resistance to diffusion computed as:
a
where C, = von Karman constant
y~ = wind speed at height 2
z- = height above the ground where meteorological
variables are determined
z, = roughness parameter - empirically derived to
account for the affect of vegetation on the flow
fields about the evaporation surface
T = surface and stoma resistance to diffusion - a
s
parallel combination of all the separate
resistances to moisture flux through the leaves
and soil surface - determined empirically -
varies seasonally according to the availability
of moisture
Evapotranspiration Model Inputs
There are three types of inputs for computation of the
amount of evapotranspiration:
207
-------
1. constants which have fixed value
2. parameters having a range of potential values which
are chosen with regard to the particular character-
istics of the evapotranspiration setting such as crop
type and size
3. climatic variables which vary as a function of the
daily, even hourly situation.
In the first category, constant values for p , L, y/ P / C , and
C-, are used for all computations of potential evapotranspiration.
T and z are both functions of the vegetative surface and as
such are chosen from experimental reference data prior to each
computation. A and d are functions of the temperature of
a
the atmosphere at a specific height above the surface and
are read and computed from tables stored within the simulation
structure. Experimental field data required for each potential
evapotranspiration prediction then includes air temperature, wind
velocity, relative humidity, barometric pressure, and the height
above the ground where they each were measured. Solar radiation
is calculated as a function of latitude and the time of year.
Sensitivity Analyses
Precise diurnal measurements of all the aforementioned
variables are not available for all situations and approximate
values must be substituted. In order to assess the sensitivity
of the model to the precise values of the various parameters,
a series of sensitivity runs were performed. Each variable was
permitted to vary over a range of typical values in order to
ascertain the effect of that permutation on the computed
208
-------
evapotranspiration value. Each variable's relationship with that
value is depicted graphically in Figures 131 to 136. In add-
ition, a sensitivity coefficient was computed for each variable
as % variation/% change in potential evapotranspiration over the
entire range of permutation to indicate numerically the relative
sensitivity of the variables. Using this approach the most
sensitive variable requiring the most precise determination is
the net solar radiation followed in decreasing order of impor-
tance by the relative humidity, surface resistance, roughness
parameter from 1-20, temperature, wind velocity, and roughness
parameter from 0-1.
Sensitivity to Net Solar Radiation (H)
The graph illustrating the effect of changing net solar
radiation values on final evapotranspiration is linear, indicating
a direct proportionality (Figure 131). As noted above, the
sensitivity analysis indicates the value for net solar radiation
to be the most critical, raising concern over choice of its
value. Two types of measurements are currently being made to
determine net solar radiation: one direct measurement using a
Fritchen type transducer, and one indirect using an Eppley Block
and White Pyronometer. Sample 1973 data indicates differences
by as much as 20% in these two types of measurement. In addition
to the measurement anomalies, using an experimental value of
net solar radiation neglects energy used for heat storage and
photosynthesis. As H is increased by 100%, potential evapo-
transpiration increases by 167% with a corresponding sensitivity
coefficient of 0.60.
209
-------
Sensitivity to Relative Humidity h
The relative humidity h is another important meteorological
variable to which potential evapotranspiration is extremely
sensitive. The calculation of the vapor pressure deficit,
d = e - e , involves the relative humidity, as d = e (1-h).
a s z as
As h is increased by 300%, potential evapotranspiration decreases
by 48% (Figure 132). The corresponding sensitivity coefficient
is -0.159. Field measurements of relative humidity are straight-
forward and should not produce significant errors in the predic-
tion of potential evapotranspiration.
Sensitivity to Surface Resistance
The surface resistance factor was varied over its range
for all situations from mature alfalfa to bare soil to prime
forest. While it is an important variable, its value can be
chosen from the data of Szeicz, et al to conform to the
particular situation and thus should not produce large errors.
As T is increased from
s
0.42 mm/hr (Figure 133).
As T is increased from .1 to 1.6, E decreases from 0.52 to
o
Sensitivity to the Roughness Parameter
The roughness parameter z, was varied in two steps: from
0 to 1, and 1-20 to minimize distortion at the lower range.
Values of z, between 0 and 1 are associated with open water, wet
soil, and mown grass, whereas values greater than 1 are associated
with alfalfa (1.4), long grass (4-9), maize (2-22), sugar cane
24
(4-9), orange groves (50), and pine forests (65-300) . As
z is changed from wet soil (0.02) to 300 cm, maize (22) poten-
tial evapotranspiration increases from 0.4 mm/hr to 0.9 mm/hr
(Figures 134 and 135).
210
-------
tr
i
2
O
<
CC
a.
o
a.
.90
.72
.54
.36
I-
2
UJ
l-
O
.18
8 12 16
NET SOLAR RADIATION (CAL/CM**2xSEC) X 1000
20
Figure 131,
.70 _
a:
i
O
H
OC
Q.
05
tr
O
a.
HI
O
a.
.56
.42
.28
.14
Potential evapotranspiration model sensitivity to
net solar radiation
18
36
54
72
90
Figure 132
RELATIVE HUMIDITY (PERCENT)
Potential evapotranspiration model sensitivity
to relative humidity
211
-------
.60 r
en .48
<
cc
o
o_
01
_i
<
LLJ
O
O_
.36
.24
.12
Figure 133,
.50
£ .40
i .30
O-
05
•2.
.20
O
Q.
.10
O
Q.
Figure 134,
8 12 16
STOM ATA/SURF ACE RESISTANCE (SEC/CM) X 10
20
Potential evapotranspiration model sensitivity
to stomata/surface resistance T
8 12
ROUGHNESS PARAMETER (CM) X 10
16
20
Potential evapotranspiration model sensitivity
to roughness parameter z, between 0 and 1 cm
212
-------
Sensitivity to Wind Speed
Wind speed U1"-3 ^n t^ie denominator of the equation for
calculating x the atmospheric resistance to diffusion. Evapo-
a
transpiration, E, contains T in both the numerator and denomina-
a
tor if T is greater than zero. Because the dependency of T
s s
on wind speed is not known, sensitivity to y was evaluated
with T = zero (Figure 136) .
o
Sensitivity to Air Temperature
Evapotranspiration potential increases linearly with
temperature (Figure 137) and is an important variable in making
accurate predictions.
Sensitivity to the Height of Meteorological Measurements - z~
The
ratio of z~ to z, appears in the calculation of
T Again T is in both the numerator and denominator of the
a ^ a
equation for E, and hence, the effect on E is not straightforward,
As z2 increases beyond 60 cm, E decreases to a nearly constant
level as the corresponding terms approach zero (Figure 138) .
213
-------
cc
I
o
I-
Q.
00
2
<
o:
o
Q.
.90
.72
.54
.36
Z
UJ
O
0_
.18
Figure 135,
12 18
ROUGHNESS PARAMETER (CM)
24
30
Potential evapotranspiration sensitivity to
roughness parameter z-.
DC
I
O
H
a:
o
a.
<
UJ
2.5
2.0
1.5
1.0
i= -5
Z
111
o
Q_
220 440 660
WIND VELOCITY (CM/SEC)
880
1100
Figure 136
Potential evapotranspiration model sensitivity
to wind speed
214
-------
.40 _
16 24
TEMPERATURE (°C)
32
40
Figure 137
2.0
Potential evapotranspiration model sensitivity
to air temperature
cc
I
s
g
i-
cr
I
I
|
UJ
I-
O
a.
1.6
1.2
.8
Figure 138
62
124 186
Z2 ELEVATION (CM)
248
310
Potential evapotranspiration model sensitivity
to height (Z2) of meteorological measurements
215
-------
SECTION VIII
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Yield. Water Resources Research. 10(3) :485-492 , 1974.
37. Yalin, Y.S. An Expression for Bed-Load Transportation.
Journal of the Hyd. Div., Proc. ASCE. 89(HY3):221-250, 1963.
38. Rowlison, D.L., and G.L. Martin. Rational Model Describing
Step Erosion. Journal of Irrigation and Drainage Division,
Proc. ASCE. 97(IR1):39-50, March 1971.
220
-------
39. Eagleson, Peter S. Dynamic Hydrology. McGraw-Hill. 1970.
40. Foster, G.R., L.. Huggins, and L.D. Meyer. Simulation of
Overland Flow on Short Field Plots. Water Resource Research,
4(6):1179-1187, 1968.
41. Moldenhauer, W.C., and D.C. Long. Influence of Rainfall
Energy on Soil Loss and Infiltration Particles:!. Effect
Over a Range of Texture. Soil Science Soc. Amer. Proc.
28 (6) : 813-817, 1964.
42. Rifai, M.N.E., W.J. Kaufman, and O.K. Todd. Dispersion
Phenomena in Laminar Flow Porous Media. Sanitary Eng. Res.
Lab. and Div- C.E. Report 3. University of California,
Berkeley. 1956.
43. Day, P.R., and W.M. Forsythe. Hydrodynamic Dispersion of
Solute in the Soil Moisture Stream. Soil Science, Soc.
Amer. Proc. 21:477-480, 1958.
44. Biggar, J.W., and D.R. Nielsen. Miscible Displacement:
I. Behavior of Tracers. Soil Science Soc. Amer. Proc.
26:125-128, 1962.
45. Rose, D.A. and J.B. Passioura. The Analysis of Experiments
on Hydrodynamic Dispersion. Soil Science 3:252-257, 1971.
46. Biggar, J.W. and D.R. Nielsen. Miscible Displacement:
V. Exchange Process. Soil Science Soc. Amer. Proc.
27;623-627, 1963.
221
-------
47. Kay, B.O. and D.E. Elrick. Adsorption and Movement of
Lindane in Soils. Soil Science 104:314-322, 1967.
48. Huggenberger, F-V. Letey, and W.J. Farmer. Observed and
Calculated Distribution of Lindane in Soil Columns as
Influenced by Water Movement. Soil Science Soc. Amer.
Proc. 36:544-548, 1972.
49. Davidson, J.M., G.H. Brusewitz, D.R. Baker- and A.L. Wood.
Use of Soil Parameters for Describing Pesticide Movement
Through Soils. Project No. R-800364. Environmental
Protection Agency, Washington, D.C. August 1974. 149p.
50. Chaudhari, N.M. An Improved Numerical Technique for Solving
Multidimensional Miscible Displacement Equations. Soc.
Petrol. Eng. J. 11:277-278, 1971.
51. Moe, P.G. Kinetics of the Microbial Decomposition of the
Herbicides IPC and CIPC. Environmental Science and
Technology. 4 (50):429-431, 1970.
52. Burshel, P. and V.H. Freed. The Decomposition of Herbicides
in Soil. Weeds. 7(2):157-161, 1959.
53. Schultz, D.P- and B.C. Tweedy. Uptake and Metabolism of
N-N-Dimenthyl-2,2-Diphenylacetamide in Resistant and
Susceptible Plants. J. Agr. Food Chem. 19(l):36-39, 1971.
222
-------
54. Freed, V.H., R.L. Zimdahl, M.L. Montgomery, and W.R. Furtick,
The Degradation of Triazine and Uracil Herbicides in Soil.
Weed Research 10(1);19-26, 1970.
55. Personal Communications with Dr. W.C. Steen, Fall 1974.
56. Spencer, W.F., M.M. Claith, and W.J. Farmer. Vapor Density
of Soil-Applied Dieldrin as Related to Soil-Water Content,
Temperature and Dieldrin Concentration. Soil Science
Soc. Amer. Proc. 33:509-511, 1969.
57. Shearer, R.C., J. Letey, W.J. Farmer, and A. Klute.
Lindane Diffusion in Soil. Soil Science Soc. of Amer. Proc.
37 (2) :189-193, March-April 1973.
58. Farmer, W.J., K. Igue, W.F. Spencer, and J.P. Martin.
Volatility of Organochlorine Insecticides from Soil:
I. Effect of Concentration, Temperature, Air Flow Rate, and
Vapor Pressure. Soil Sci. Soc. Amer. Proc. 36:443-447, 1972,
59. Igue, K., W.J. Farmer, W.F. Spencer, J.P. Martin.
Volatility of Organochlorine Insecticides from Soil:
II. Effect of Relative Humidity and Soil Water Content on
Dieldrin Volatility. Soil Sci. Soc. Amer. Proc.
36:447-450, 1972.
60. Farmer, W.J., K. Igue, and W.F. Spencer. Effect of Bulk
Density on the Diffusion and Volatilization of Dieldrin
from Soil. J. Environ. Qual. 2:107-109, 1973.
223
-------
61. Mayer, R., J. Letey, and W.J. Farmer. Models for Predicting
Volatilization of Soil-Incorporated Pesticides. Soil. Sci.
Soc. Amer. Proc. 38:563-567, 1974.
62. Carslaw, H.S., and J.C. Jaeger. Conduction of Heat in
Solids, Second Edition, Oxford University Press. 1959.
63. Penman, H.L. Natural Evaporation from Open Water. Proc.
Roy. Soc., London, 1948. p. 120-145.
64. Van Bavel, C.H.M., Potential Vaporation: The Combination
Concept and Its Experimental Verification. Water
Resources Research. 2:455-467, 1966.
65. Szeicz, G., G. Endrodi, and S. Jajchman. Aerodynamic
and Surface Factors in Evaporation. Water Resources
Research. 5(2) :380-394 , 1969.
224
-------
APPENDIX A
USER'S GUIDE TO SCRAM
SCRAM was programmed to allow the user flexibility through
the use of sequential data input and namelist data inputs.
Table A-l lists the program job control language set up. The
user needs to set up a library with the program module. To
the cards listed in Table A-l, the user must supply the
library data set name, sequential data input and namelist
input. Table A-2 describes the sequential data required by
SCRAM including rain history and meteorology. This data is
required for every event to be simulated. Table A-3 lists
and describes the elements in the namelist input option.
By selecting the proper options and supplying the proper
parameter values, the user is able to run any event or sequence
of events he desires.
225
-------
User's Guide to SCRAM (Continued)
Table A-l. SCRAM JCL SET UP
INPUT DESCRIPTION
PROGRAM JOB CONTROL LANGUAGE SETUP
// JOB
// EXEC GOSTEP,LIB='Your Library Name1
//GO.FT11F001 DD UNIT=SYSDA,SPACED(TRK,(1,1))
//GO.FT12F001 DD UNIT=SYSDA,SPACE=TRK,(1,1))
//GO.FT04F001 DD *
(Sequential Data Input)
//GO.SYSIN DD *
&PESTI
(Namelist Input Data)
SEND
226
-------
User's Guide to SCRAM (Continued)
Table A-2. SEQUENTIAL DATA INPUT
RAINFALL CARDS
Card 1 - Header
Col 1-4 - 'RAIN'
Col 11-4 - Units flag for Rain Gauge
0 = cm
1 = mm
2 = m
3 = in
4 = ft
Card 2 - Number of watershed zones or subplots
col 1-5 NZN (15)
Card 3 - Multiplying factors for rainfall rate on
each zone
Col 1-80 - RMF (I) , I = 1, NZN
[IF RMF(I) = 1.0 program 13 is the same
as ESL 967. CONTM]
[If RMF(I) = -1.0, user must specify
raingauge cards for each zone]
Cards 4,5,6, - Raingauge data cards
Col Description
1—4 Year
;: ^ „ "7, May be Omitted if Same as Previous Card
6-7 Month -*
9-10 Day
12-13 Hour
15-16 Minute
18-19 Second
21-32 Rain Gauge Reading
227
-------
User's Guide to SCRAM (Continued)
Card 2 - Multiplying factors for each zone
Col 1-4 = EMF(l) (F4.0)
Col 5-8 = EMF (2)
: :
: EMF(NZN)
Col 77-80 EMF(20)
If all EMF(i) =1.0 program runs as
ESL 967. CONTM
Cards 3,4,5. . . Environmental Data
Col Description
1-19
23-32
33-44
45-56
57-68
69-80 )
Data - Same as Rain Cards
Wind Velocity
Air Temperature
F12.0 Cloud Cover
Barometric Pressure
Relative Humidity
228
-------
User's Guide to SCRAM (Continued)
Table A-2,
Card 1 - Header
Col 1-4
Col 12
Col 14
Col 16
Col 18
Col 20
SEQUENTIAL DATA INPUT (continued)
EPA WEATHER DATA CARDS
'DAYS' or 'NITE' Indicate Whether Data
is for Day or Night. (Day Value Used
if No Night Data Specified.)
Units Flag for Wind Velocity
0 = cm/sec
1 = m/sec
2 = ft/sec
3 = mph
4 = knots
Units Flag For Air Temperature
0 = 'C
1 = °F
Units Flag For Cloud Cover
0-10 Scale
Units Flag for Barometric Pressure
0 = mb
1 = atmospheres
2 = PS1
Units Flag for Relative Humidity
0 = Fractional Hunidity
1 = Percent
229
-------
User's Guide to SCRAM ( Continued)
Table A-3. NAMELIST INPUT DATA
ARRAY/
DIMENSION
PLOTNM
(5)
PESTNM
(5)
STARTM
(6)
ENDTM
(61
PRINT
(3)
ELE2
RUFF
SRES
DELGAM
(121)
SVPRES
(121)
DHARAY
(1520)
THETA
(27,20)
ZONES
(14,20)
ELEMENT
1-5
1-5
1-5
1-5
1
2
3
+ 1
+ 2
+ 2 + N
+ 2 + 2IM
I, J
1, I
2, I
3, I
4, I
5, I
6, I
7, I
8, I
9, I
10, I
11,1
12, I
13, I
14, I
DEFAULT
VALUE
BLANKS
BLANKS
0
0
600.
600.
86400.
0.
0.
0.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
DESCRIPTION
20 CHARACTER WATERSHED NAME
20 CHARACTER PESTICIDE NAME
SIMULATION START TIME
YEAR, MO, DAY, HR, MIN, SEC
SIMULATION END TIME
YEAR, MO, DAY, HR, MIN, SEC
OUTPUT PRINT INTERVALS, SEC
DURING RAIN
NO RAIN, SOIL MOIST
NO RAIN, SOIL DRY
ELEVATION 2 ""l
ROUGHNESS PARAM. CONSTANTS
SURFACE RESISTANCE USED
PARTIAL OF DELTA I BY
W.R.T. GAMMA ( EVAPOTRANSPIRATION
SATURATION VAPOR MODEL
PRESSURE J
DHTAB TABLE INPUT
SOIL TYPE NUMBER (1-10)
NUMBER POINTS IN ARRAYS (N)
N THETA VALUES
N DIFFUSIVITY VALUES
N PRESSURE HEAD VALUES
SOIL MOISTURE PROFILE
WATERSHED ZONE DEFINITION
SOIL TYPE NUMBER
1 LT CLAY
2 = SERL LOAM
3 =
AREA
SLOPE, PERCENT
LENGTH
AVERAGE WIDTH
BULK DENSITY
NO. INCREMENTS (USED FOR SEDIMENT MODEL)
NO. LAYERS (USED FOR INFILTRATION MODEL)
LAYER THICKNESS
MAXIMUM RUNOFF VELOCITY
UNITS FLAG FOR AREA
0 = cm2
1=ft2
2 = ACRES
UNITS FLAG FOR LENGTH, WIDTH
0 = cm
1 =ft
UNITS FLAG FOR LAYER THICKNESS, RUNOFF RATE
0 = cm, cm/SEC
1 = ft, ft/SEC
UNITS FLAG FOR BULK DENSITY
0 = gm/cm3
1 = Ib/ft3
230
-------
User's Guide to SCRAM (Continued)
Table A-3. NAMELIST INPUT DATA (Continued)
ARRAY/
DIMENSION
RUNOFF
(2, 4, 20)
CON
(50)
IOPT
(50)
ENG
ALFA
DV (27, 20)
DIST(27, 20)
ELEMENT
1,1, J
2, 1, J
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
DEFAULT
VALUE
0
0
1.168E-3
582.
6.1 E-4
0.48
2.5
0
0.100
1
1
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
DESCRIPTION
ZONE RUNOFF DEFINITION
(FOUR PAIRS PER ZONE)
ZONE TO WHICH RUNOFF GOES
PROPORTIONAL AMOUNT
PROGRAM CONSTANTS
MASDEN )
LTHEAT USED BY
BOWEN > EVAPOTRANSPIRATION
SHEATP MODEL
VONK J
THRSH1 - RAINFALL RATE THRESHOLD
THRSH 2 - SOIL MOISTURE THRESHOLD
WD- WEIGHT DENSITY (SEDI)
DTMIN - MINIMUM DELTA T IN SIMULATION
K -\
RHO
T
NEXP CONSTANTS USED
AB \ BY ADDE
CO f
PULSE
DVS
D J
ALIM SEDIMENT LIMIT
ALAT LATITUDE OF SUBPLOT
MSR MAXIMUM SOLAR RADIATION
KOPT^
MOPT
TOPT I CONSTANTS
TMAX f USED BY
AK DEGR
BK J
CANOPY COVER - USED IN ADJUSTMENT OF K3 - ST
PROGRAM CONTROL OPTIONS
COLD START OPTION
0 = COLD START
1 = WARM START
1=0 TO PREPARE FOR WARM START
=£0 TO WRITE NAMELIST DATA
1=0 TO PRINT DHTAB ARRAYS
=£0 CARD PUNCH FOR CALCOMP PLOTS
1=0 TIME 0/P FROM BALANC
1=0 O/P AT RAINFALL CHANGE TIMES
= NO READ - USED BY ADDE
= N # PRINTER -O/P
= 1 O/P EVERY CYCLE
¥O DO NOT CALL DEGR IN MAIN
¥=0 DO NOT CALL ADDE IN MAIN
1=0 DO NOT CALL VOLT IN MAIN
1=0 DO NOT O/P WHEN IDRY = 0
=r=0 DO O/P AT PRINT (1) EXACTLY
=5^0 DO VOLATILIZATION O/P ONLY
NANOGRAMS PESTICIDE APPLIED (VOLT)
APPLICATION RATE (VOLT)
DIFFUSION COEFFICIENTS (VOLT)
PESTICIDE PROFILE BY ZONE (VOLT)
231
-------
APPENDIX B
SCRAM PROGRAM LISTING
(FORTRAN IV, IBM 370)
MASTER SCHEDULER
ADDE
BALANC
DATEIN
DATINTI
DATOUT
DEGRAD
EVAP
FILTR
INPUT
ITABLE
NEWRAP
OUTPLT
OUTPUT
PRNTTM
RK
RUNGE
SED
SEQDAT
SETUP
SIMPSN
SOLAR
VOLT
VPRNT
WATER
232
-------
SCRAM Program Listing (Continued)
SIMULATION OF CONTAMINANT REACTIONS AND MOVEMENT ISCRAMI
PeiTlCIDE SIMULATION PROGRAM MASTER SCHEDULER
-OMMON /TIMtS/ TOLU,TNEW,DT,DTOLD,TOUT,TSTRT.TSTOP,TRAIN,PIN,
L EPATM, PRINT13), PROGDTI3), PFSTM ,OATPL,DATHAT,DATHAR
*EAL*8 TOLD,TNE».DT,DTOLD,TOUT,TSTFT,TSTQP,T.^AIN,PIN,EPATM,PESTM
r
-------
SCRAM Program Listing (Continued)
JFUOPTl Ib) .EO.OI TNEW=TRAIN
IFITNEW-TOLO .GT. OTRO) TNEW = TOLD+DTRO
I F(TNEW.LT.TOL)T) TOUT= ( I DI Ml TNEW/P I N I + 1. POJ »PIN
_ALL FILTR
INEW TOLO+OT
'.ALL SED
25 iF (TNEW .GT. EPATM) CALL DATEPA
.ALL EVAP
aQ TO 40
C RAKING
33 PIN PI MR
IDRY 2
TNEW = DMINilTRAIN,TOUT)
IFIIOPTI15).EU.O) TNEW=TRAIN
IF (TNEW-TOLO .GT. OTWETI TNEW TOLD + OTHET
IF(TNEW.LT.TOUT) TOUT= (I DI NT (TNEW/PIN)+ 1 .DO) *PI.M
CALL FILTR
TNEW = TOLD+DT
IF (TNEW .GE. PESTM) KPEST = 1
oALL SED
40 IF (KPEST .EQ. 0) GO TO 41
1F(IOPT(13) ,NE.O» GO TO 51
UALL VOLT
51 iFJIOPTllll. NE. 0) GO TO 50
CALL DEGRAD
50 IFCIOPTl121.NE.O) GO TO 41
CALL ADDE tIDRY)
41 CALL BALANC (IDRY,KPEST)
TOLD TNEW
JTOLD = DT
IF (TNEW .GE. TRAIN) GO TO 44
42 IF (TNEH .LT. TOUT .AND. lOPT(lO) .EQ. 0) GO TO 43
1F(IOPT(14I.EQ.O .OR. IDRY. NE.O) CALL OUTPUT(2)
TOUT =(IDINT(TNEW/PINI + l.DO)*PIN
GO TO 48
44 CONTINUE
GO 13 1*1,NZN
U RAINOI II- RAINRI I )
..ALL DATIN
00 15 1=1,NZN
IFIRAINOII).NE.O) GO TO 15
IFIRAINRU l.Efl.O) GO TO 15
CALL OUTPUTI5)
iO TO 45
Ib CONTINUE
45 IF UOPU7) .EQ. 0) GO TO 42
LALL OUTPUT(3)
TOUT =(ID1NT(TN£W/PIN)*1.DO)*PIN
48 IF (TOLD .LT. TSTOP) GO TO 10
C FINISHED
I.ALL OUTPUT(4)
CALL OUTPLT
KEWIND 11
KEWIND 12
00000550
00000560
00000570
00000580
00000590
00000600
00000610
00000620
00000630
00000640
00000650
00000660
00000670
00000680
00000690
00000700
00000710
00000720
00000730
00000740
00000750
00000760
00000770
00000780
00000790
00000800
00000810
00000820
00000830
00000840
00000850
00000860
00000870
00000880
00000890
00000900
00000910
00000920
00000930
00000940
00000950
00000960
00000970
00000980
00000990
00001000
00001010
00001020
00001030
00001040
00001050
00001060
00001070
00001080
234
-------
SCRAM Program Listing (Continued)
l»0 TO 1 00001090
cND 00001100
235
-------
SCRAM Program Listing (Continued)
00001110
C 00001120
C 00001130
C VA-UABLES ARE INT1ALIZED AND DEFAULT VALUES ARE SET IN THIS ROUTINE 00001140
C 00001150
00001160
00001170
00001180
00001190
00001200
00001210
00001220
00001230
00001240
00001250
00001260
00001270
00001280
00001290
COMMON /WATERD/ NZN, RAINRI20), THETA (27 ,20) , THETN (27,20» .CUMRO 00001300
i ,CUMFLT,OHTA8(50,4,10),NUMDH<10).RINFI20),CIT(20),VELC(27,20)00001310
i ,0(27.20),SUMRN,WATROT,SUMIN,ROR,ROT ,XUNRO 00001320
00001330
COMMON /SEOATA/SUB(10,20),ADJLII21),ADJLO(20),RNF(4,20I,INF(4,20) 00001340
L ,SEDRAT,HECT,AK1I10),AK2(10),ST(10),AOJLL
<. ,XADJLI
aLOCK DATA
VA-UABLES ARE INT1ALIZED AND DEFAULT VALUES ARE SET IN THIS ROUTINE
COMMON /INPUTD/ STARTHC6),ENOTM(6),PLOTNM(5),PESTNMI5),
I PESDAT(lll,CROPOT(10),ZONES<14,20) .RUNOFF(2,4,20)
COMMON /CONST/ C(JN( 50) , IOPT ( 50) .KPEST .NZPREV, NZERO
COMMON /EVAPIN/ ELE2,DATA(5,20), DATAN(5,20),
I RUFF,SRES,DELGAM(1211,SVPRES(1211,VPRE2,VPOEF,
2 ATRES.POEVAP.TOTVAP
COMMON /TIMES/ TOLD,TNEW,OT.DTOLD,TOUT,TSTRT.TSTOP,TRAIN,PIN,
1 EPATM, PRINT(3), PROCDT(3), PESTM ,DATPL,OATMAT,DATHAR
*EAL*8 TOLD, TNEW, OT.DTOLO,TOUT, TSTRT.TSTOP, TRAIN, PIN, EP ATM, PESTM
}EAL*8 OATPL.DATNAT.DATHAR
COMMON /ADOATA/ C(27,20), S(27,20), KNT, SSSI27.20)
00001350
00001360
00001370
00001380
1 ,OC(27», VEL(27), THETJI27), B(27),KDES(27,20),CMAXUM(27,20),00001390
1 THETX.XMAX, H. KTIME.II, A, DENOM.OENAM,INDEX(20),INDEX1(20),00001400
3 ANT, AX, I1SAVE. IGOR, NVALI20) .OESKRO, XPONT.KLEW1 (20) ,OVST, 00001-410
4 THETAT, SUMC(27I, SUMS(27),CUMAD, CUHDS, PTOT(20) ,C1(27,20) 00001420
VPAST(27,20),KSH(20),INTGER ,NOSTOP(20),AURX,DSRX
t> ,TOTAD,TOTOS,ZROC(27,20) , CCL ( 27 ) ,S SL( 27 ) ,TOT ( 27 )
COMMON /VOLTD/ ENG,ALFA.OV(27,20),01ST(27,20) ,I VI,PPB(27 ,20 ) ,
I OVSI27,20) ,P2
,ADJLO/20*0./ .SEDRAT/0./
10*79.298, 10*.3418E-2, 10*8.
DATA SU8 /200*0./
JATA ADJLl/2l*0./
JATA AK1, AK2, ST/
DATA STARTM /6*0./
JATA ENOTM /6*0./
JATA PLOTNM /5*1 •/
JATA PESTNM /5*1 •/
JATA PESOAT /5*0., 1999., 1., 1., 3*0./
jATA CROPOT/0.,1999.,1..1.,1999.,1.,1.,1999.,!
JATA ZONES /280*.0/
JATA RUNOFF /160*0./
JATA PRINT /600..600. , 86400./
JATA PROGDT /60., siOO.,3600./
JATA CON /l.lobE-3, 582., 6.1E-4, 0.48,
i l.E-5. 0.25, I., 1.,
t. 0.4. 1.53, 0., 1., 2.5, 90.,
.1. /
2.5,
13., 0.. 3.1,
00001430
00001440
00001450
00001460
00001470
00001430
00001490
00001500
00001510
00001520
00001530
00001540
00001550
00001560
00001570
00001580
00001590
00001600
00001610
00001620
00001630
00001640
236
-------
SCRAM Program Listing (Continued)
*
J
f
f
F
F
*
V
F
*
*
»
f
F
*
F
F
*
F
*
F
F
*
F
F
F
F
F
F
F
F
2., 33., .008,
.119676, .173599, 38.2344,
.9, 22*0. /
JATA IOPT/7*0,2,U41*0/
JATA KPEST/0/
JATA ELE2, RUFF, SRES /121.9, 0.
JATA TOTVAP /O./
JATA DELGAM /
0.670, o.69u, 0.
0.360. 0.69G. u.
1.100. 1.130, 1.
1.380, 1.420, 1.
1.730, 1.780, 1.
2.140, 2.200, 2.
2.640, 2.710, 2.
3.230, 3.310, 3.
3.930, 4.030. 4.
4.750, 4.860, 4.
5.700, 5.830, 5.
6.800, 6.950, 7.
8.070, 8.240, 8.
9.520. 9.720, 9.
11.200. 11.400, 11.
13.100/
JATA SVPRES /
4.579, 4.750, 4.
6.101, 6.318. 6.
8.045, 8.323, b.
10.518, 10.870. 11.
13.634, 14.076, 14.
17.535, 18.085, 18.
22.377, 23.060, 23.
28.349, 29.184, 30.
35.663, 36.683, 37.
44.563, 45.799, 47.
55.324, 36.810, 58.
68.260, 70.050, 71.
83.710, 85.850, 88.
H02.090, 104.650,107.
'123.320, 126.810,129.
F
149. 380/
JATA THETA /540*0./
JATA THETN /540*0./
JATA CUMRU /O./
DATA CUMFLT /O./
OATA SUMIN /O./
JATA OHTAfl /
6.00E-J2, 8.JOE-02,
1.80E-01, 2.JOE-01,
3. OOE-01, 3.20E-01,
4.20E-01, 4.40fc-01,
l.OOE-07, l.OOE-06,
7.30E-05, 9. OOE-05,
7. OOE-04, 8. OOE-04,
720
920
160
460
820
260
780
400
120
970
, 0.740,
, 0.940,
, 1.200,
, 1.500,
, 1.380,
, 2.320,
, 2.850,
, 3.480,
, 4.220,
, 5.090,
960, 6.090,
100
420
920
600
926
543
609
231
530
, 7.260,
, 8.600,
, 10.100,
, 11.900,
, 5.107,
, 6.775,
, 8.905,
, 11.604,
, 14.997,
650, 19.231,
756
043
729
067
340
880
020
200
820
1.
2.
3.
4.
6.
1.
9.
, 24.471,
, 30.923,
, 33.801,
, 48.364,
, 59.950,
, 73.740,
, 90.240,
,109.860,
,132.950,
OOE-01, 1
20E-01, 2
40E-01, 3
60E-01, 4
OOE-06, 1
50E-04, 3
OOE-04, 9
39.9065,
02, 0.5/
0.760,
0. 970,
1.230,
1.550,
1.930,
2.380,
2.920,
3.570,
4.320,
5.200,
6.230,
7.410,
8.770,
10.300,
12.100,
5.294,
7.013,
9.209,
11.987,
15.477,
19.827,
25.209,
31.824,
39.898,
49.692,
61.500,
75.650,
92.510,
112.510,
136.080,
.20E-01 ,
.40E-01,
.60E-01,
.80E-01,
.OOE-05,
.OOE-04,
.50E-04,
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0.790,
1
1
1
1
2
3
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6
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0
1
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.280, 118.
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40E-01
60E-01
80E-01
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OOE-05
30E-04
OOE-03
,
,
,
,
,
,
,
1
2
4
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300, 1.
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530, 4.
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600, 12.
685, 5.
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844, 10.
788, 13.
477, 16.
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695, 34.
175, 43.
442, 53.
800, 66.
600, 81.
200, 99.
040,120.
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.60E-01,
.80E-01,
.OOE-01,
840,
060,
340,
680,
090,
580,
150,
840,
640,
570,
650,
900,
330,
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800,
889,
775,
176,
205,
999,
714,
535,
667,
355,
867,
510,
650,
650,
920,
990,
27*0.,
5
6
1
.30E-05,
.OOE-04,
.30E-03,
00001650
00001660
00001673
00001680
00001690
00001700
00001710
00001720
00001730
00001740
00001750
00001760
00001770
00001780
00001790
00001800
00001810
00001820
00001830
00001840
00001850
00001860
00001870
00001880
00001390
00001900
00001910
00001920
00001930
00001940
00001950
00001960
00001970
00001980
00001990
00002000
00002010
00002020
00002030
00002040
00002050
00002060
00002070
00002080
00002090
00002100
00002110
00002120
00002130
00002140
00002150
00002160
00002170
00002180
237
-------
SCRAM Program Listing (Continued)
1.60E-03, 1.30E-OJ, 2.00E-03, 7.00E-03, l.OOE-02, 27*0.t 00002190
-6.00E 05.-9.00E J4.-4.00E 04,-l.OOE 04.-7.00E 03.-4.70E 03, 00002200
-2.00E 03.-1.Out 03,-S.OOE 02.-6.80E 02.-5.70E 02.-4.50E 02, 00002210
-3.JOE 02.-2.20E J2.-1.00E 02.-9.00E Ol.-7.70E 01.-6.00E 01, 00002220
-5.00E G1.-4.00E 01.-2.00E 01,-l.OOE 01, 0.0 , 27*0., 00002230
50*0., 1800*0./ 00002240
JATA C.S.SUMC,SUMS/ 540*0., 540*0., 27*0., 27*0./ 00002250
JATA CUMAO. CUMOS XO.,0./ 00002260
DATA KNT/0/ 00002270
OATA VEL,NVAL/27»J., 20 *1 / 00002280
DATA PTOT.C1/ 20*0., 540*0 ./ 00002290
JATA CMAXUM, VPAST , KSW, INTGER, IGOR /540*0., 540*0.,20*0,0,O/ 00002300
OATA NOSTOP/20*0/ 00002310
OATA KLE^l, INDEX,INDEX1 /20*1, 20*2, 20*2 / 00002320
3ATA RDT,ADJH./2*U./ 00002330
OATA XUMRO/0./ 00002340
DATA TOTAD,TOTUS/2*0./ 00002350
JATA XADJLI/0./ 00002360
UATA ZROC/540*0./ 00002370
OATA IV1, ENG, ALFA, DV/ 0,7000., 1.0, 540*0/ 00002380
JATA D1ST /I.,26*0.,1.,26*0.,1. ,26*0.,1.,26*0. ,1.,26*0.,1.,26*0., 00002390
« 1.,26*0.,!.,26*0.,1.,26*0.,1. ,26*0.,1.,26*0.,1.,26*0., 00002400
« 1..2b*0..l.,26*0.,l.,26*0.,1.,26*0.,1.,26*0.,1.,26*0.. 00002410
* 1.,26*0.,!..26*0. / 00002420
OATA NZPREV, NZERO/0,O/ 00002430
OATA CCL,SSL.TOT/27*0.,27*0.,27*0./ 00002440
END 00002450
238
-------
SCRAM Program Listing (Continued)
10
SUBROUTINE ADDElldRY) 00002*60
00002470
.01MON /«ATERD/ NZN, RAINR, THETAI27,20 I.THETN(27,20 I,CUMRT 00002480
1 , CUMFLT,DHTAcH50,4,10) ,NUMDH( 10) ,RINF(2J) , C 1 T (20 I, VELC ( 27, 20 100002490
2 ,0(27.20),SUHKN,WATROT,SUMIN,ROR 00002500
00002510
COMMON /ADOATA/ C(^7,20), 5(27,20), KNT, SSS(27,20) 00002520
I .DC127), VELI27), IHETJI27), 8(27), KDES(27,20),CMAXUM(27,20)00002530
<: ,THETX,XMAX,H,KTIME, II ,A,OENOM,DENAM,INDEX!20), INOEXK20), 00002540
$ ANT, AX, JISAVE, IGOR, NVALI201.DESKRO, XPONT.KLEW1(20),DVST,00002550
4 THEIAT, SUMC(27), SUMS(27),CUMAD, CUMOS,PTOT (20) ,C1(27,20) 00002560
a , VPAST(27,20) ,KSW(20) .INTGER
COMMON /CONST/ CQN( 50 ) , IOPT (50 I
EQUIVALENCE (CON112),T)
EQUIVALENCE I NOREAO, I OPTI 8) I
00 10 1=1, NZN
*OREAO=0
IF(NVAL(I 1.EQ.2) NOREAD=2
i,ALL CONTAMJ1 , IDRr)
DO 15 1=1, NZN
IF(PTOTII) .GT. .001) RETURN
3 CONTINUE
,NOSTOP(20).AORO.DSRO
If PESTICIDE IS GONE, IN MAIN
00 NOT CALL ADOE IOPT112)
00 NOT CALL DEGR IOPT<11)
NE
NE
IDPTt 111 = 1
10PT( 12) = 1
RETURN
END
00002570
00002580
00002590
00002600
00002610
00002620
00002630
00002640
00002650
00002660
00002670
00002680
00002690
00002700
00002710
00002720
00002730
00002740
00002750
00002760
239
-------
SCRAM Program Listing (Continued)
00002770
00002780
00002790
03002800
00002810
00002820
00002830
00002840
00002850
00002860
COMMON /WATERD/ NZN, RAINR120), THETA<27,20),THETN(27,20),CUMRO 00002870
I ,CUMFLT,OHTAB(50,4,10I ,NUMDH(IO) .RINFI20) ,CI T ( 20 I, QTOT ( 27, 20100002380
00002890
00002900
00002910
1 EPATM, PRINTI3I, PROGDT13), PESTM ,DATPL,DATMAT,DATHAR 00002920
*EAL*8 T OLD, TNE W, OT, DTOLO, TOUT, TS TR T, T STOP, TRAI N,P I N, EPATM, PESTM
*EAL*8 DATPL,DATMAT,OATHAR
SUBROUTINE BALANC ( I DRY , KPEST )
SuoriOUTINE TO ACTUALLY MOVE rfATER, SEDIMENT, AND PESTICIDE.
ALiJ CALCULATES TOTAL AMOUNT OF WATER AND PESTICIDE TO CHECK
AGAINST PREVIOUS AMOUNT.
COMMON /SEDATA/SUd(10,20),AOJLI(21),ADJLOI 20),RNF14,20),INF(4,20)
I .SEDRAT.HECT.AKU 10) , AK2110) , S T (10) , ADJLL
<- .XADJLI
,0(27,20),SUHRN,HATROT,SUMIN,ROR,RDT ,XUMRO
COMMON /TIMES/ TOLD,TNEW,DT.DTOLD,TOUT,TSTRT,TSTOP,TRAIN,PIN,
COMMON /EVAPIN/ ELE2, DATA15.20), DATAN(5,20),
i RUFF,SRES,DEL6AM(121),SVPRES(121),VPRE2,VPDEF,
i ATRES.POEVAP.TOTVAP
COMMON /CONST/ CONi501,IOPT(50),KDUMM,NZPREV, NZERO
COMMON /AODATA/ 0(27,20), SI27.20), KNT, SSS{27,20)
00002930
00002940
00002950
00002960
00002970
00002980
00002990
00003000
00003010
00003020
,DC(27), VEU27), THETJ(27), B( 27) ,KDESl 27, 201 ,CMAXUM( 27,20) ,00003030
THETX.XMAX, H, KTIME.II, A, OENOM.DENAH,INDEX(20 I,INDEX1(20),00003040
ANT, AX, IISAVE, IGOR, NVAL(20).OESKRO, XPONT.KLEWl(20),DVST,00003050
THETAT, SUMC(27», SUMS(27),CUMAD, CUMOS,PTOT(20) ,01(27,201 00003060
VPAST(27,20),KSW(20).INTGER ,NOSTOP(20).AORX.OSRX
,TOTAO,TOTDS,ZROC(27,20),CCL(27),SSL(27),TOT<27)
DIMENSION SUMTH(20)
NAMEHST/8UG1/C.S,THETN
^AMELIST/BUG2/EXX,RDT,AOJLL,CBAR,OSRO,SBAR,AORO,ES,EC
1 .CUMAD.CUMDS .CUMRO
',. .XUMRO.TEMPAD,XADJLI,TEMPOS
PTOTV TOTVAP
PRO = CUMRO
PSED = ADJLK21)
WATROT = CUMRO
ADRX 0.
USRX 0.
ROT 0.
ADJLL = 0.
MOVE PESTICIDE
MOVE SEDIMENT t RUNOFF
JO 10 1=1,NZN
ADJLII I) = 0.
THETAI1,1) = 0.
iUMTHtl) THETNI1.I)
IZN - SUB(8,I)
00003070
00003080
00003090
00003100
00003110
00003120
00003130
00003140
00003150
00003160
00003170
00003180
00003190
00003200
00003210
00003220
00003230
00003240
00003250
00003260
00003270
00003280
00003290
00003300
240
-------
SCRAM Program Listing (Continued)
JO 10 J=2,IZN
IHETAI J. 1) = THETNIJ, I )
iUMTH(I) SUMTH(I) *• THETNU.II
10 -ONTINUE
9 IF (IDRY .EQ. 0) GU TO 35
: EMU- MONTHS SINCE PESI. DATE
tMCh= (TOLD-PESTM)/ ( 6 O.*60. *24.*30. )
EXX= EXP(-EMO)
DO 25 1 = 1,NZN
C CHt^K FOR MAX RUNOFF RATE
HOMAX = SUB<1J,I)*OT*THETN(1,I)
IF (THETNU.l) .LE. ROMAX I GO TO 12
12
THETAI1,I)
THETNI 1,1)
00 20 J=l,4
IF (INF(J.l)
*F (INF1J.1I
THETA<1,1)
RUMAX
THETNtl.I)
ROMAX
.LE. 0) GO TO 20
.LE. 20) GO TO 15
C ACCUMULATE RUNOFF AND
C CriftNGE TO LITERS
CUMRO = CUMRO + THETNI 1,I)*RNF(J,I)*SUB(2,1 I*SUb(9,11 /1000.
XUMRO = XUMRU * THETNil,I)*RNF(J,I)*SUB(2,I)*SUB(9,I 1/1000.
*OT = ROT «• THETNl 1,1 )*RNF( J.I )*SUB(2, I )*SUB<9, I 1/1000.
ADJLL = AOJLL + ADJLO(I) * RNF(J.I)
XADJLI= XADJLI* AOJIOU) * RNF(J.I)
liO TO 18
15 THETAd, INF(J.n) = THETA (I , INFU, I) ) + THETNl 1, I ) *RNF U , I)
1 *SUS(2.II*SUB(9, I)/(SU8(2, INFU, I I I *SUB< 9, INFl J , I) ))
18
20
25
AOJLK INFU, 1)1
CONTINUE
CONTINUE
CALCULATE
00 42 1 = 1,KM
LCL(I) - 0.
SSLI I) - 0.
DO 41 J=1,NZN
AOJLM INF(J,II) + ADJLO(I)*RNF( J, I)
TOTAL C AND S VALUES FOR EACH LAYER
CCL(I>
iSL(I)
oCLI I)
iSLd )
roT( i)
s
CCL(
SSL(
1)
I)
COMPUTE
= CCLU )
= SSL( I)
= CCL( I)
•f
4-
C( I
S(I
,
,
J)*THETN(
J)*SUB(6,
1 + 1
J)
AVERAGES
/ NZN
/ NZN
* SSL (I )
CALCULATE AMT. OF PESTICIDE IN RUNOFF AND SEOIMENTt MICROGRAMS)
LBAR = ILCLd) * CCLJ2D/2.
USRO-RDT*2.2E-4 *CBAR*EXX
SBAR =
-------
SCRAM Program Listing (Continued)
MA«.E ADJUSTMENTS TO FIRST LAYER OF C AND S
CALCULATE TOTAL AREA OF WATERSHED SU.CM.
UNITS OF DSRO C ADRO —GRAMS
CHANGE TO HICROGRAMS
JDDO=DSRL)*l.E + 6
AAAA = ADRO
TAREA = 0.
JO 40 1=1,NZN
4J TAREA = TAREA «• SUb(2,I)
CALCULATE PESTICIDE BALANCE
JO 43 1=1,NZN
FRACTION OF PESTICIDE LOSS FROM SPECIFIC WATERSHED
8Y FRACTION OF AREA
4REA2 = SUB(ZiI) / TAREA
TOTAL GRAMS OF PESTICIDE DISSOLVED IN LAYERS 1 AND 2
cTOT = (CI1.II*THETN(2,I)*C(2,I)*THETN(3,I)1
STOT = (S(1,I)+S(2,I))*SUB(6,i)
C
C REMOVE PESTICIDE FROM TOP 2 LAYERS
IFIETOT.EQ.O.) GO TO 83
:<1,I) = (C(1,I)*THETN(2,I)-C(1,I)*THETN(2,I)/ETOT*DODD/TAREA)
1/THETNI2.I)
CI2,I) = IC(2, I)*THETN(3,11-1C(2,I)*THETN<3,1 I/ETOT*DDDD/TAREA))
1/THETNO,! J
83 IFISTOT.EO.O.) GO TO 43
S(1,I) (Sll,I)*SUB(6,I)-AAAA/TAREA*S(l,II/STOT)/SUB(6,I)
1(2,1) (S(2,I»*SUB(6,I)-(AAAA/TAREA*S(2,I)/STOT))/SUB(6,I)
43 CONTINUE
C
C BALANCE WATER
C
35 00 24 1=1,NZN
C WAItR IN = SUM(THETAIO) + RAINR*DT)
RAIN RAINR(I)*DT»SUBI2,I 1/1000.
SUMIN = SUMIN + RAIN
SUMRN = SUMRN * RAIN
C WAIER OUT SUMITHETA * RUNOFF)
24 WATROT WATROT + SUMTH(I)*SUB(2,I)*SUB(9,I)/1000.
99 CONTINUE
c CALCULATE RUNOFF AND SEDIMENT RATES
*OR = (CUMRO-PROl/DT
SEDRAT = { ADJLH21J-PSED)/OT
C INCLUDE EVAPOTRANSPIRATION AND INFILTRATION LOSS TO WATER OUT
WATROT = WATROT + CUMFLT * PTOTV
iF MOPT16) .NE. 0)
*CALL DATOUT (TNEW.0,01
,>JZPREV=N2fcRO
JO 205 1=1,NZN
KR THETN(I,IJ*SU6(9,I)/DT
00003850
00003860
00003870
00003880
00003890
00003900
00003910
00003920
00003930
00003940
00003950
00003960
Q0003970
00003980
00003990
00004000
00004010
00004020
00004030
00004040
00004050
00004060
00004070
00004080
00004090
0000-4100
00004110
00004120
00004130
00004140
00004150
00004160
00004170
00004180
00004190
00004200
00004210
00004220
00004230
00004240
00004250
00004260
00004270
00004280
00004290
00004300
00004310
00004320
00004330
00004340
00004350
00004360
00004370
00004380
242
-------
SCRAM Program Listing (Continued)
cLF AOJLO(I) / (SUB15.II*OT) 00004390
IFKRfc.Eg.O.).AND. (ELF.E3.0. )l GO TO 205 00004400
,.&LL OUTPUI15) 00004460
20b'uONTINUE 00004470
riETURN 00004480
cNO 00004490
243
-------
SCRAM Program Listing (Continued)
iUBROUTlNE CONTArtlNZ, IDRY1
SUo*3UTINE TO PREDICT THE SIMULTANEOUS CONCENTRATION OF PESTICIDE
AuiORBED AMD IN SOLUTION WITHIN THE SOIL MATRIX.
C=«6SOLUTE CONCENTRATION OF SOLUTE
S=40SORBED VALUES
Nt*P = THE CONSTANT tXPONENT ON THE TERM C**N,NEXP=N
AB= THE CONSTANT IN THE EXPONENT ON THE TERM C**(1/AB)
USED FOR OESOR.PTION
VCL= VELOCITY
RHU = BULK DENSITY OF SOIL
K= THE CONSTANT K
D= JIFFUSION COEFFICIENT
DVS = CONSTANT DIFFUSION COEFFICIENT FOR VAPOR PHASE OF C.USED FOR
00004500
00004510
00004520
00004530
00004540
00004550
00004560
00004570
00004580
00004590
00004600
00004610
00004620
00004630
00004640
INFILTRATION AND REDISTRIBUTION TO CALCULATE SURFACE FLUX OF CHEMICA00004650
CQ= MAGNITUDE OF INPUT PULSE
PULi£= THE DISTANCE IN THE SOIL OF THE LEADING EDGE OF AN INITIALLY
DISTRIBUTED CHEMICAL
COMMON/CONST/CON(501,1 OPT(50)
COMMON /SEDATA/SUBUO,20)
REAL K,KTIME,NEXP,KRHO,KOES,KOND,INFILT
DIMENSION VPAST<802),CLORID(B02),TTIMER(200),DELXHT(200I
iOMMON /TIMES/ TOLD,TNEH,DT,DTOLD,TOUT,TSTRT,TSTOPtTRAIN,PIN,
1 EPATM. PR1NU3), PROGDTI3), PESTM
KEAL*8 TOLD,TNEW,DT.DTOLD,TOUT,TSTRT.TSTOP,TRAIN,PIN.EPATM,PESTM
COMMON /ADDATA/ C(27,20), S(27,20), KNT, SSSI27.20)
00004660
00004670
00004680
00004690
00004700
00004710
00004720
00004730
00004740
00004750
00004760
00004770
00004780
00004790
00004800
1 ,DC(27), VELI27I, THETJI27), B(27),KDES<27,201,CMAXUM{27,20),00004810
<: THETX.XMAX, H, KTIME.II, A, DENOM.DENAM, INDEX ( 20) , INDEX L (20) , 00004820
3 ANT, AX, IISAVE, IGOR, NVALI20).DESKRO, XPONT.KLEHII20),DVST,00004830
» THETAT, SUMC127), SUMSl27).CUMAD, CUMDS,PTOT(20) ,C1(27,20I 00004840
3 , VPAST(27,
-------
SCRAM Program Listing (Continued)
H5u K.OESU ,1 J= DfcSKRQ
7711 iONTINUE
JJ 1
JXT 2
JAY 1.
RHO = K »NEXP
FHETX= THETJI1H
CHECK EVAPOTRANSPIRATION ONLY FLAG
IF YES, GD TO ROUTINE TO CALCULATE NEW CONCENTRATIONS00005510
DEPENDING ON THE NEW THETA VALUES CALCULATED 00005520
IF IIDRY.EU.O) GO TO 6000 00005530
IF INOREAD) 590,590,600 00005540
CONTINUE 00005550
00005560
WRITE(6,580) IC(JiNZI,J=l.KUICK) 00005570
00005040
00005050
00005060
00005070
00005080
00005090
30005100
00005110
00005120
00005130
00005140
00005150
00005160
00005170
00005180
00005190
00005200
00005210
00005220
00005230
00005240
00005250
00005260
00005270
00005280
00005290
00005300
00005310
00005320
00005330
00005340
OOU05350
00005360
00005370
00005380
00005390
00005400
00005410
00005420
00005430
00005440
00005450
00005460
00005470
00005480
00005490
00005500
245
-------
SCRAM Program Listing (Continued)
iFINOCLOR . E(J. 1) READ(5,580) ( CLOP ID ( J ) , J=l, KUI CK)
HRITEI6.580J ( S( J.NZ) , J=l .KUICK)
IF(KSWINZ) .GT.O) GO TO
rsOUIT = 0
JO 1210 1=1,11
J= II - I + 1
IF(KQUIT) 1220,1220,1230
1220 THEK= S(J,NZ) /ICU.NZ) **NEXP)
IFKTHEK .GT. 1.015*K) .OR. (THEK .LT. .985*K)) KQUIT = J
1FIKOUIT .EQ.OI GO TO 1210
1230 CMAXUMU.NZI = CO
-------
SCRAM Program Listing (Continued)
TOTC1 + C(l.NZ) * THETAl I + It NZ ) * SU.NZ) * SUB(6,NZ)
CO
JO 470 1=1, II
470 TOTC1
00 21
21 t( I,NZI = Cl( l.NZI
CALL RUNGEI lit KRHOiNZ)
IF(NOREAO.EO.O) C(1,NZ)
00 22 1=1, II
Zi CKI.NZ) = C(l.NZ)
TIME' TIME » KTIME
*NV=NVAL(NZ)
id TO <<«bO, 160) , MNV
48J 1F( PTOT(NZ) .GE.TI GO TO 60
uO TO 160
1140 OC(JJ)- KHO/THETJ( JJ)
CALCULATE VELOCITY
00 396 J = JXT, II
XTHET" (THETJIJ+l) - THETJCJ-1) 1/2./H
OERIV= (VEHJ) -VPASTI J.NZ) ) /KT IME/THET J( J)
VPAST(J,NZI> VEL(J)
VEL(J) = VEL( J)/THETJ( J)
»G= (-VEL(J) *VEL(J» /THETJIJ) »XTHET-DERI V) *KTIME/2 .
Li' VEL(J) -D»XTHET/TH£TJ(J»
B(J)=- (ZZ-GG)*KTIME/H
390 UC(J)= RHO/THETJ(J)
VPASTd.NZI' VEL(l)
VELUJ) ' VEL( JJ)/THETJ(JJI
A' ADKH* KTIME
VAVGR> (VELIU *VEL(2) I*. 5
JENOM=(D*.08*VAVGR(/(D*( . 08«-H ) *VA VGR I
JENAM' IOABSO*.08*VEL( II ) ) / ( DABSD+I H+ . 03 1 *VEL ( 1 1 ) )
GO TO 460
60 INOEXINZi- 2
'•4VALINZI-2
160 CONTINUE
tfRITE(6.30J A1,A4,K,BTIME,THETAT,RHO,A2,CO,AB,NEXP
WRITE<6,50001 D. A4
00 171 J-l.IISAVE
1FiO TO 171
00006120
00006130
00006140
00006150
00006160
00006170
00006180
00006190
00006200
00006210
00006220
00006230
00006240
00006250
00006260
00006270
00006280
00006290
00006300
00006310
00006320
00006330
00006340
00006350
00006360
00006370
00006380
00006390
00006400
00006410
00006420
00006430
00006440
00006450
00006460
00006470
00006480
00006490
00006500
00006510
00006520
00006530
00006540
00006550
00006560
00006570
00006580
00006590
00006600
00006610
00006620
00006630
00006640
00006650
247
-------
SCRAM Program Listing (Continued)
363 i * .5 00006860
5 C(I.NZ) (CHI.NZ) » C1U + 1.NZI) * 0.5 00006870
C(II.NZ) = CKII.NZ) * 0.5 00006880
S(II.NZ) - 5(11,NZ) * .5 00006890
IF (NOREAO.EO.O) GO TO 175 00006900
C CALCULATE TOTAL UG OF PESTICIDE AFTER RUNGE 00006910
LL = 2 00006920
5005 CONTINUE 00006930
L = LL 1 00006940
IF(ZROC(L,NZI.NE.O.) C(L.NZ) = 0. 00006950
1F(CIL,NZ).GT.O.I GO TO 5004 00006960
CKL.NZ) = 0. 00006970
S(L,NZ) 0. 00006980
LL = LL + 1 00006990
IF(LL.GT.II) GO TO 220 00007000
GO TO 5005 ; 00007010
500<» CONTINUE 00007020
TOTC = 0. 00007030
00 25 I=LL,I1 00007040
25 TOTC= TOTC + C(I,NZI*THETN(1+1,NZ) * SII.NZ) * SUBJ6.NZ) 00007050
TOTC1 = TOTC1 - TOTC 00007060
IF1TOTC1.GT.O.) GO TO 5003 00007070
TOTC1 TOTC1 * TOTC 00007080
L LL-1 00007090
C(L.NZ) 0. 00007100
CKL.NZ) 0. 00007110
S(L,NZ) = 0. 00007120
ZROC(L.NZ) - 1. 00007130
LL = LL + 1 00007140
GO TO 5004 00007150
5003 CONTINUE 00007160
C CALCULATE NEW CK1.NZ) 00007170
ID = 2 00007180
L = LL - 1 00007190
248
-------
SCRAM Program Listing (Continued)
i-OLO = CIL.NZ)
LALL NEwRAPIL.NZ.iO.TOTCl.COLO)
;i(L,NZ) = 2. * CIL.NZ) - Cl(LL.NZ)
SIL,NZ>- K.DES(L,NZ)*AB*C(L,NZJ**(l./ABI
2t CONTINUE
17:> CONTINUE
PTOTCNZI = 0.
JO 230 1 = 1,II
230 PTOT(NZ) PTOT(NZ) + C(I.NZ) * THETNI I + 1 ,NZ)
C 17y .ALL SIMPSN I SUM,IISAVE,NZ)
C rfRITE16,82) SUKC(NZ).SUMSINZ).SUM
iF(NOCLuR.ECi.l) WK1TE<6,91) SUMCL
KETURN
CONTINUE
DURING EVAPORATION ONLY, CALCULATE NEW VALUES OF C
USING NEW THETA VALUES
NEW VALUE OF C BY THE NEWTON- RAPHSON TECHNIQUE
220
C
C
6000
C
C CALCULATE A
ID = 1
tlO 6010 L^l,II
IF(CIL.NZ) .EQ. O.J GO TO 6010
IOTC1 » CIL.NZI * THETJIL1 * S(L.NZ) * SUBI6.NZ)
CALL NEWRAPCL.NZ,ID.TOTC1.COLO)
6010 CONTINUE
C
C SET INDEX FLAG FOR ADSORPTION VS DESORPTION... WANT ONLY ADSORPTION
C
C
C GO TO ROUTINE THAT CALCULATES SORBED CONC. FROM SOLUTION CONC.
C
00007200
00007210
00007220
00007230
00007240
00007250
00007260
00007270
S(I.NZ) *SUB(6,NZI00007280
00007290
00007300
00007310
00007320
00007330
00007340
00007350
00007360
00007370
00007380
00007390
00007400
00007410
00007420
00007430
00007440
00007450
00007460
00007470
00007480
00007490
00007500
00007510
00007520
00007530
00007540
00007550
GO TO 160
10 FORHATI6I5)
20 FORMATI5F15.5I
30 FORMATC19X, • VEL* ' ,F 10.3.5X, • D= • , F10.3.5X,'K='.F10.3 ,5X ,
*'KTIME=«1.E10.3f5X,
-------
SCRAM Program Listing (Continued)
fl . l.Ot THE CALCULATIONS HILL PROCEED USING THIS VALUE.',/) 00007740
38o FORMAT!IX./,5X,'FOR THE PARAMETERS: K=',F12.4,', NEXP=«,F12.4,•, C00007750
»J='.F12.-4,', RHO=',F12.4,', THETA=',F12.4,//,5X,-AND FOR AN INITIA00007760
*LLY DISTRIBUTED PULSE, CONVERGENCE DID NOT OCCUR ON THE ITERATIVE•00007770
»./,5X,'CALCULATIONS OF THE INITIAL C/CO AND S/CO VALUES. AFTER 10000007780
* ITERATIONS THE VALUE OF C/CO= ',E14.7,/I 00007790
391 FORM4Tl5X./,5X,'THt CALCULATION OF C/CO IS OUTSIDE THE ALLOWED RANOOC07800
• liE.'./.SX,'THEREFORE, YOU MUST READ IN THE VALUES Of C/CO AND S/C000007810
* ON CARDS. THIS CAN 3E DONE BY SETTING THE VALUE OF NOREAD=1',/I 00007820
58u FORMAT(aFlG.O) 00007830
99B FORMAT(1X,/,1X,'fOR THE ABOVE, INFILTRATION RATE= '.F12.7,1, #»# 00007840
» CUMULATIVE INF1LTRATION= '.F12.7,' INFILTRATION DELT, TINCER=',F100007850
»
-------
SCRAM Program Listing (Continued)
iUBROUTINE DATEINIOI.DPSEC)
Jill) = YEAR
iJI(2) = MONTH
01(3) = DAY
Jl<5> = MINUTE
01(4) = HOUR
JII6) = SECONDS
JPSEC = DOUBLE PRECISION SECONDS FROM JAN 0,
1900
(INPUT!
(INPUT)
(INPUT)
(INPUT)
(INPUT)
(INPUT)
(OUTPUT)
JQUBLE PRECISION
UIMENSION 1(5)
DIMENSION
oATA
OPSEC.Y
J/31,59,90,120,151,181,212,243,273,304,3347
DO 1 K'1,6
IF (DI(K) .GT. 0.) GO TO 3
1 CONTINUE
-------
SCRAM Program Listing (Continued)
IF tlOPTUI .EQ. 0) GO TO 5 00012260
READ (14) ADJLI, CUMP.O, KPEST, THETA, C,S, TSTRT.TOLD 00012270
1 , CUMDS,CUMAD,VPAST,CMAXUM,NOSTOP,INDEX,INDEX1,KLEWI,KDES,KSH 00012280
i. .XUMRU.cl, ICTADt TOTDS, XADJLI .uT iCIT 00012290
3 ,TNEW,SUMRN,SUMIN,TCTVAP,CUMFLT,NVAL ,THETN 00012300
OTOLD= DT 00012310
5 IF (IOPT(3).NE.O) WRITE!6,PESTI) 00012320
CALL DAIEIN (STARTM,TSTRTI 00012330
CALL OATEIN (ENDTM,TSTOP) 00012340
KRITEI6.1000) PLOTNM,PESTNM 00012350
100J FORMAT (•!', SOX -BEGIN PESTICIDE SIMULATION'// ' WATERSHED N00012360
IttME: ',5A4// • PESTICIDE NAME: ',5A4// ' START DATE:1) 00.012370
uALL DATOUT(TiTRT,D,0) 00012380
*RITE<6,1001) 00012390
1001 FORMAT«'0', 'END DATE:') 00012400
uALL DATOUTITSTOP.D.O) 00012410
C CALCULATE NUMBER OF ZONES 00012420
00 20 1=1,20 00012430
IF (ZONES(ltl) .EO. 0.) GO TO 25 00012440
20 CONTINUE 00012450
,MZN = 20 00012460
(.0 TO 30 00012470
25 UZN = 1-1 00012480
IF (NZN .LE. 0) CALL ERROR<4) 00012490
30 CONTINUE 00012500
C INITIALIZE TIMES 00012510
IFdOPTl 1J .NE.O» GO TO 100 00012520
TNEH=TSTRT 00012530
TOLD=TSTRT 00012540
10U CONTINUE 00012550
CALL DATEIN (PESDAT(61.PESTM) 00012560
C CHANGE DATES TO OP SEC, DATPL IS DATE PLANTED 00012570
C DATHAR IS DATE HARVESTED 00012580
C DATMAT IS DATE OF MATURITY 00012590
JO 81 J=l,3 00012600
81 FEMP(JI= CROPDTU + 1) 00012610
CALL DATEINITEMP.OATPL) 00012620
00 82 J=l,3 00012630
82 IEMP(JI= CROPDT(J*4) 00012640
CALL DATEIN1TEMP,DATHAR) 00012650
DO 83 J=l,3 00012660
83 TEMP(J»= CROPDT(J*7» 00012670
CALL DATEINITEMP,DATMAT) 00012680
dRITEI6,2000) 00012690
2000 FORMATC OPLANT DATE: •) 00012700
CALL DATOUT«DATPL,0,0) 00012710
rtRITE(6,2001) 00012720
2001 FORMATI'OMATURITY DATE: •) 00012730
CALL DATOUT 00012750
2002 FORMAT COHARVEST DATE: ') 00012760
CALL DATOUTC UATHAR,D,0) 00012770
C SET UP SUB-PLOT DESCRIPTION 00012780
C 00012790
262
-------
SCRAM Program Listing (Continued)
Suo(J,I) IS DEFINED AS FOLLOWS:
SJol 1,1) = SOIL TYPE
Suo(2,I)=AREA(CH**2I
iUd(3,I)=SLOPE(*)
SUd(4, I )=L tNliTHlCM)
S0d(5. I)=WIOTH(CM)
So8(6,I)=BULK DENSITY(GM/CM**3)
SJD(7.I)-NO. OF INCREMENTS FOR SED MODEL
Sjrt(8tI)=NU. OF LAYERS
iub<9.I)=LA»ER THICKNESS(CM)
11=1,NZN WHERE NZN=NU. CF ZONES)
yRITE(6,1002)
1002 FORMAT I'O' ,47X,'WATERSHED ZONE DEFINITION1// • ZONE f'.ZX,
i'SOIL TYPE1, SX.'AREA1, 8X,•SLOPE',7X,'LENGTH1,7X,'HIDTH',6X,
i'DENSITY'.SX, 'SEDIMENT1, 9X,«NO.', 8X,'LAYER'/ 85X,•INCREMENTS',
i 6X,•LAYERS1, 5X, 'THICKNESS' / 25X,'CM»»21, 7X,'PERCENT', 8X,
4 'CM' ,11.x,'CM' , 6X, 'GM/CM**2«,34X, 'CM' // I
HECT 0.
00 40 1=1,NZN
DO 35 J=l,10
35 SUB( J,II - ZONES! J,I )
SUB(3,I) = SUBI3,11/100.
c CHANGE UNITS IF NECESSARY
IF IZONESdl,!) .NE. C.) SUB(2,I) = SUB ( 2 ,1) *CONV (I NT I ZONES (11,1 I
1)
IF (ZONES (ii,l) .EQ. 0.) GO TO 36
bUBCt.I) - SUBI*. I)*CONVI3)
iUB(5,I) = SU815,IJ*CONV(3)
36 IF (ZONES<13,1) .EQ. 0.) GO TO 37
J = ZONES! 13,1) «• 2
iUB(9,I) = SUBI9.I )*CONV( J)
iUBdO.IJ = SU8( 10,I>*CONV< J)
37 IF (ZONES! l
-------
SCRAM Program Listing (Continued)
4F ( INFl J.I) .GT.21) CALL ERROR (7)
-------
SCRAM Program Listing (Continued)
00 63 1=1,10
IF IDHTABI1,1,I) .EO. 0.) GO TO 63
•RITE (6,1006) I
lOOb FORMATl'l1, SOX 'OHTAB ARRAY, SOIL TVPE',13//
I 20X-THETA-.lOX'UlTHETA) DI FFUS I VI TV • , 3X 'H(THETA)
I, 6X 'SIGMA 0 DELTA THETA' //I
M = NUMDHII)
WRITE (6,100 7 I (J, (DHTAB(J,K,I) ,K=1,4),J=1,N)
1007 I-ORMAT
-------
SCRAM Program Listing (Continued)
10
20
100
50
FUNCTION JTABLE 20, 100, 50
N3 = N2
liO TO 10 •
ITABLE N2
RETURN
Nl = N2
t,0 TO 10
END
OOOK160
00014170
00014180
00014190
00014200
00014210
00014220
00014230
00014240
00014250
00014260
00014270
00014280
00014290
00014300
00014310
00014320
00014330
00014340
00014350
00014360
00014370
00014380
266
-------
SCRAM Program Listing (Continued)
SUBROUTINE NEW RAP I L, NZ , I 0 ,ALF, CEST 1
COMMON /AOUATA/ CUT,20), S(2T,20), KNT, SSS(2T,20I
00014390
00014400
00014410
.00(27). VELI2T), THETJ12T), B(27),KDESC27,20),CMAXUM(27,20),00014420
THETX.XMAX, H, KTIME.II, A, DENOM.DENAM,INDEX(20),INDEX1(20),00014430
ANT, AX, USAVE, IGOR, NVALl 20) .DESKRO, XPONT.KLEW1<20),DVST,00014440
THETAT, SUMCU7), SUMS (27) ,CUMAO, CUHDS, PTOT (20) ,C1I27,20) 00014450
VPAST(27,20»,KSW(20) ,INTGER ,NOSTOP(20) , ADRO.DSRO
COMMON /WATERD/ NZN, RAINR(20I,
.1L,NZ)= 0.0
RETURN
20 t,(L«NZ)= CNEW
RETURN
cND
NEwRAP, NO CONVERGENCE AFTER 20 STEPS, LAYER='
=* ,I5/ 10X, ' C(L(NZ)= ',E15.6,3X,' CNEW=' E15.6)
00014930
00014940
00014950
00014960
00014970
00014980
00014990
00015000
00015010
268
-------
SCRAM Program Listing (Continued)
iUBROUTINE OUTPLT
MArt.cS PRINTER PLOTS AND PUNCH" TAD-IS FOR PLCTS OF:
TIME VS RUNOFF
T IME VS RUNOFF RATE
TIME VS RUNOFF/RAINFALL
TIME VS SEDIMENT
uUMMON /PLOTS/ K.TPLT , ARAY {100,9) , PC ( 27 I
COMMON /CONST/ CON(50),IOPTI50)
COMMON /AODATA/ C(27,20), 5(27,20), KNT
DIMENSION VMAX(7),VMIN(7)
JATA VMAX.VMIN /14*0./
DATA ANEG/-1./
DIMENSION YLISTdOO)
DATA YLIST /100*1.E30/
DIMENSION TITL120.8)
OATA TITL / 4*' •,
I . 'SEDI't•HENT',' LOA','0 (K1,'G/HE','CTAR•,•E) ',13*' ',
6 'SEDI'.'MENT', VRUN'.'OFF ' , • (GM/ • , • LITE' , • R) ', 9*' ',
7 4*« '.'PEST1, •. LO'.'SS 0','N SE'.'O. •, II*1 ',
8 4*1 •.•PESI«.«. LO'.'SS I'.'N H2','0 '. 11*' • /
iF((IOPT(9).EQ.lt. AND. (IOPTUO) . EQ. 0 )) RETURN
IF (KTPLT .EQ. 01 GO TO 99
WRITE (6,1000)
DO 5 1=2,7
VMINd ) = 1.E30
5 VMAXU ) = 0.
DO 10 1=1,KTPLT
ARAYII.U - ARAYI 1,1) + 5.
riRITE (6,10011 I, (ARAYd, Jl , J=l,9t
DO 10 J = 2,9
t/MINIJ) = AMINK VMIN(J), ARAYI I,J) )
VMAX(J) = AMAXKVMAX(J),ARAYd,J) )
10 CONTINUE
VMINd) * INT(ARAY(l,l)/25. )*25
VHAXdJ = dNTlARAY(KTPLT,11/25.) + l)*25.
IF (VMAX(4) .EQ. VMINI4)) GO TO 15
VMINK) = 0.
VMAX(4l = 100.
15 CONTINUE
DO 20 J=2,7
00015020
00015030
00015040
00015050
00015060
00015070
00015080
00015090
OOC15100
00015110
00015120
00015130
00015140
00015150
00015160
00015170
00015180
00015190
00015200
00015210
00015220
00015230
00015240
00015250
00015260
00015270
00015280
•00015290
00015300
00015310
00015320
00015330
00015340
00015350
00015360
00015370
00015380
00015390
00015400
00015410
00015420
00015430
00015440
00015450
00015460
00015470
00015480
00015490
00015500
00015510
00015520
00015530
00015540
00015550
269
-------
SCRAM Program Listing (Continued)
IF (VHIMJJ .EQ. VMAX(J)) GO TO 20 00015560
CALL PPLOT (TITLl 1, J-l), ARAY (1,1) ,ARAY{1,J),KTPLT,YLIST,VMINt lit 00015570
1 VMAX(l)tVMINlJ),VMAX(J),1) 00015580
20 CONTINUE 00015590
C PUNCH CARDS IF REQUESTED 00015600
IF UOPT<5) .EO. 0) GO TO 99 00015610
VMAXO) = VHAX(3)*faO. 00015620
JO 35 1 = 2.^ 00015630
IF(([.EQ.2).OR.(I.EQ.41.0R.II.E0.5I.OR.II .EQ.61) GO TO 35 00015640
IF (VMINMI .EO. VMAX(ll) GO TO 35 00015650
«!RITE (7,1002) VMAX(l) 00015660
1002 FORMAT <• TIME (MINI ' , 34XF 1 0. 0.24X • 10*) 00015670
JRITE(7,10031(TITLlJ,1-1),J=5,11),VHAX(I) 00015680
1003 FORMATI7A4,16X F10.3,24X'28•) 00015690
DO 30 J=1,KTPLT 00015700
iF 1I.EQ.3) ARAYU.3) = ARAYIJ,3)*60. 00015710
30 HRITEI7,10041 ARAriJ,l),ARAV(J,I) 00015720
1004 FORHATC2F9.2) 00015730
*RITE<7,10041 ANEG.ANEG 00015740
35 CONTINUE 00015750
C PUNCH CARDS FOR X PESTICIDE 00015760
rfRITEl 7, 20001 00015770
2000 FORMAT!1 PROFILE UEPTH VS % PESTICIDE ') 00015780
DO 100 I-ltKNT 00015790
£1=1 00015800
HRITEC7, 1004) EI.PCUI 00015810
100 CONTINUE 00015820
i
-------
SCRAM Program Listing (Continued)
SUBROUTINE GUTPUTUTYP)
v-OMMON /TIMES/ THl f,, TNFW.OT ,OTOLO, TOUT , TSTRT , TSTOP, TR A I N , P IN ,
i. EPAIM
r*EAL»8 TOLD,TNE*,DT,OTOLD,TCJT,TSTRT,TSTCP,TRAIN,PIN, EPATM
00015900
00015910
00015920
00015930
00015940
00015950
COMMON /SEDATA/SUD(1D,20> ,ADJL 1(21),40JLO(20), RNF<4 ,20 ) , I NF ( 4, 20) 00015960
1 .SEiJkAT ,hECT,AKl( 10) ,AK2<10) ,STUO) .ADJLL 00015970
2 .XAOJLI 00015980
00015990
COMMON /nATERD/ iMZN, RAINRI2D), THET A( 27 ,20) , THETN ( 27 ,20 1 , CUMRO 00016000
I ,CUMFLT,OHTAb(50,4,lO>,NUMDH(10),RINF120),CIT(20),QTOT(27t20100016010
,Q127,20I,SUMRN,WATROT,SUMIN,ROR,ROT ,XUMRO
COMMON /fcVAPIN/ ELE2, DATA(5,20), OATANI5.20),
i RUFF.SRES.DELGAMt 12 1 I , SVPRES < 12 1 ) , VPRE2 , VPDEF ,
2 ATRES.POEVAP,TOTVAP
COMMON /CONST/ CON150),IOPT(50).KPEST
00016020
00016030
00016040
00016050
00016060
00016070
00016080
00016090
COMMON /ADDATA/ C(27,201, 5(27,20), KNT, SSS(27,20) 00016100
,OC(27), VEH27), THETJI27), B( 27 I , KDES( 2 7, 20) , CMAXUMI 27, 20) , 00016 110
THETX.XMAX, H, KTIME.II, A, DENOM,DENAM,INDEX(20),INDEX1(20),00016120
ANT, AX, 1ISAVE, IGOR, NVAH20 I ,DESKRO, XPONT,KLEW1(20 I,DVST,00016130
THETAT, SUMCI27), SUMS(27),CUMAO, CUMOS,PTOT(20) ,C1(27,20) 00016140
, VPAST(27,20),KSW(20),INTGER ,NOSTOP(20),ADRX,OSRX
,TOTAD,TOTDS,ZROC(27,20),CCL(27),SSL<27),TOT(27)
COMMON /PLOTS/ KTPLT,ARAY(100,9) ,PC(27)
REAL*8 TOO /O./
JIMENSION 0(3)
KEAL*8 TYPE15)/'
1 'SPECIAL '/
DATA IKT /-!/
INITIAL•
NORMAL1,'RAINFALL'.•
FINAL',
IOPT116) NE 0 IS TO PRINT VOL ITALIZAT ION OUTPUT ONLY
IFUOPT(la) .NE.OI RETURN
XSEDKG= XADJLI/1000.
SEDKG == AOJLH21I/1000.
iKT = IKT + 1
iFI(ITYP.EQ.3) .OR. (ITYP.EQ.51) GO TO 207
IF (MOO(IKT, IOPTI91) .NE. 0 .AND. ITYP .NE. 4) GO TO 30
207 CONTINUE
-------
SCRAM Program Listing (Continued)
REWIND 13
*RITE (13) ADJL1, CUMRO, KPEST, THETA, C, S ,TSTRT,TOLD
i , CUMOS.CUMAO.VPAST.CHAXUM.NOSTOP,INDEX,iNDExi,KLEHi,KDES,KSH
i .XUMRO.Cl, TOTAD, TOTDS, XAOJLI ,DT ,CIT
3 .TNEW.SUMRN,SUMIN,TCTVAP,CUMFLT,NVAL ,ThETN
999 CONTINUE
IF (ITYP.EQ.ll GO TO 30
C OUIPUT SEDIMENT LOAD DISTRIBUTION
-RITE <6,lOol)
LOOi FORMAT<'l ZUNE * SEDIMENT1,8X 'RUNOFF*. 2X,'TOTAL' /
1 14X 'LOAD* ,11X 'RATE', 3X, 'PESTICIDE1 /
<. 11X 'GM/CM/SEC',9X 'CM/S', 3X, 'MICROGRAMS' //)
ISW=1
00 20 1=1,NZN
*R = THETNI1,I)*SUB(9,II/DT
ELF = AOJLOU) / (SUB(5, I ) *DT )
IFKRR.NE.O. I .OR. (ELF.NE.O. I) I SW=0
20 rfRITE<6, 1002) UELF.RR, PTOT(I)
1002 FORMATII 10.6E12.4)
I FUOPTI 12) .NE.OI GO TO 202
rfRITE<6,20001
200U FORMAT)'0',12X,'AVERAGE*,5X,'AVERAGE*,7X,'TOTAL1/
i 3X,'PKOFILE',3X,'PESTICIDE',3X,'PESTICIDE',3X,'PESTICIDE'/
2 5X,'DEPTH'.ax.'DJSSOLVEO',4X,'ADSORBED'/
* 12X,"MCCROGRAMS1,2X,•MICROGRAMS' ,2X, 'MICROGRANS1)
VALJES OF C AND S ARE BEFORE ADJUSTMENT TO FIRST LAYER
CILAYER.ZONEI
KNT IS MAX # OF LAYERS FOR C AND S
TPC=0.0
JO 40 1 = 1, KNT
PC(II = TOTII)
FPC = TPC + TOTd I
«RITE(6,1002) l.CCLIII.SSL(I),TOT(I)
'i-O 1.0NTINUE
DO 400 1=1,KNT
400 PC(I)= PC(I)/ TPC *100.
22 CONTINUE
KATOS=0.
RATAO=0.
IFIRDT.NE.O.) RATDS = DSRX/RDT *l.F+6
1FIADJLL.NE.O.) RATAD= ADRX/ADJLL
XDS= CUMDS /HECT
XAD= CUMAO*l.E-6 /HECT
202 CONTINUE
«RITE<6,10031 XUMRO, XDS, RATDS, XSEOKG, XAD, RATAD
00016440
00016450
00016460
00016470
00016480
00016490
00016500
00016510
00016520
00016530
00016540
00016550
00016560
00016570
00016580
0,0016590
00016600
00016610
00016620
00016630
00016640
00016650
00016660
00016670
00016680
00016690
00016700
00016710
00016720
00016730
00016740
00016750
00016760
00016770
00016780
00016790
00016800
00016810
00016820
00016830
00016840
00016850
00016860
00016870
00016880
00016890
00016900
00016910
1003 FORMATCO*. 3X, -ACCUMULATED RUNOFF:1, 33X, 'ACCUMULATED PEST ICID00016920
IE LOSS:',18X,'INSTANTANEOUS PESTICIDE LOSSV8X, 00016930
i 'WATER =' ,F12.0, 'LITERS', 28X,'IN WATER =',F12.2, 00016940
A'GRAMS/HECTARE',5X, F12.2, 'MICROGRAMS/LITER• / 00016950
J 5X, 'SEDIMENT =',F12.0,'KILOGRAMS',22X, 00016960
4 'ON SEDIMENT =',F12.2, 'GRAMS/HECTARE'. 00016970
272
-------
SCRAM Program Listing (Continued)
a 5X, F12.2, 'K1CRCGRAMS/GRAM' / )
: <*L*,J = RATE UF LOSS (UG/G/HR)
'. RLUj= RATE OF LOSS (UG/L/HR )
*LA0= (RATAO/OT) * 3600.
RLDS= (RATUS/CT) «3600.
: CJiui2) is ANT OF PESTICIDE APPLIED i uc/CM*»2)
: PCftU,PCOS= i OF THE AMT OF PEST APPLIED
: ARC* - TOTAL ARfcA OF WATERSHED (CM*»2)
ARCM=0.
JO 31 1=1,NZN
31 ARCM= ARCM + SUB(2,I)
k,OSDS=CUMJS»l.E + 6
CADAO=CUMAD
PCDS = ICDSDS/(CON112)« ARCM))»100.
PCAD= (CADAD/I CON I12)*ARCM))*100.
•RIT616,1004) PCDS, RLDS, TOTVAP, PCAO, RLAD
1004 FORMATCO TOTAL WATER LOSS', 36X, •* OF PESTICIDE APPLIED'
I 'RATE OF PESTiCIDt LOSS'/7X,'FROM EVAPOTRANSPIRATION'
c ,30X,'IN WATER =', F7.4, 23X, F12.2,
i 'HICROSRAMS/LITEK/HR' / 7X, •=', F12.0, ' LITERS',
* 30X,'ON SEDIMENT =', F7.4.23X,
-------
SCRAM Program Listing (Continued)
99 CONTINUE 00017520
IFIISW.NE.il RETURN 00017530
XUMRO=0. 00017540
XACJLI=0.0 00017550
oUMAD=0. 00017560
CUMDS=0. 00017570
RETURN 00017580
END 00017590
274
-------
SCRAM Program Listing (Continued)
SUBROUTINE PRNTTH
C
c SUDK.OUTINE TO PRINT VALUES OF THETA, CIT, c, ANO s
c
COMMON /SEDATA/SU81 10,20) ,ADJL I(21),ADJLO(20),RNF(4,20)
C
COMMON /hATERC/ .NZN, RAINR120), THETA ( 27 ,20) , THETN ( 27,
1 ,CUMFLT,DHTAB(50,4,10),NUMDH(10),RINF(2J),CIT(20),
<: ,0(27,20)
COMMON /ADDATA/ 0(27,20), 5(27,20), KMT
NTH =MAX VALUtS OF iMfcTA (NO. OF LAYERS) = SUfl(B.I)
TO PRINT OUT VALUES OF THETA ( NTH , NZN)
*RITE(6, 1000)
1000 FORMATC'O', SOX'ZONE DEPTH PROFILE')
40
50
310
320
321
340
330
333
430
440
450
460
470
400
51
TH= 1.0
00 40 1=1, NZN
TH = MAX11TH,SUB(8,I ) )
NTH=TH
ISW=1
NA=1
NB=NZN
IF(NB.LE.IO) GO TO 50
NB = 10
CONTINUE
rfRITE (6,310)
FORMATCO1 ,55X,'ZONE #• )
MRITE (6.320) (I1.I1=NA,NB>
FORMAT! • PROFILE1, 3X, 10<5X, 12,4X1
MRITEI6.321)
FORMAT!1 THETA')
UO 340 12=1, NTH
*RITE (6.330J 12 , ( ( THETA ( I 2 , II ) , I1
FORMAT(3X, 12, 4X, 10F11.3 )
WRITE I 6, 333) (CIT ( 1 1 1 , 1 1=NA, N8)
FORMAT!' CIT '.10F11.3)
IF (KNT .EQ. 0) GO TO 400
URITE (6.430)
FORMAT!' DISSOLVED PESTICIDE')
00 440 12=1, KNT
URITE (6,450) 12, ( C ( I 2 , 1 1 ) , 1 1 = NA, NB)
FORMAT(3XI2,4X10E11.3I
• RITEI6.460)
FORMAT!1 ADSORBED PESTICIDE')
UO 470 12=1, KNT
HRITE(6,450) I 2 , « S ( I 2 , 1 1 ) , 1 1=NA,NB 1
aO TO (51,521 , ISH
if (NZN.LE.10) GO TO 52
NA= NB-H
1^8= NZN
ISW 2
GO TO 50
= NA,NB)
00017600
00017610
00017620
00017630
,INF(4,20) 00017640
00017650
20I.CUMRO 00017660
QTOTI27,20)00017670
00017680
00017690
00017700
00017710
00017720
00017730
00017740
00017750
00017760
00017770
00017780
00017790
00017800
00017810
00017820
00017830
00017840
00017850
00017860
00017870
00017880
00017890
00017900
00017910
00017920
00017930
00017940
00017950
00017960
00017970
00017980
00017990
00018000
00018010
00018020
00018030
00018040
00018050
00018060
00018070
00018080
00018090
00018100
00018110
00018120
00018130
275
-------
SCRAM Program Listing (Continued)
5
-------
SCRAM Program Listing (Continued)
FUNCTION RK(M.T)
c
C FimCTIfJN SUBPROGRAM CALLED BY SUBROUTINE DEGR
C
C
kEAL M,MOPTtK,KOPT
C
COMMON /CONST/ CON150)
cQUIVALENCE (CON(221,KOPT),
-------
SCRAM Program Listing (Continued)
iUBROUTINE RUNGE(L,KRHO,NZ)
-------
SCRAM Program Listing (Continued)
no
60
UdiNZI* A6S(U(2,NZI*DENOMI
IFdREDIS .E(j. 2) u(l.NZ) = U( 1, NZ I -OVS*U (I, NZ )**/H
J(L.NZ) = ABSCUIL2,NZ)«DENAMI
IF(NOSTOPINZ) .Eg. 1) GO TO 270
CHAX = 0
Kl= KLEHKNZ)
00 60 I = K1 ,L2
IF(CMAXUM( I ,MZ) . LT . U(I,NZ» CMAXUMI I ,NZ t» U I I, NZ )
IF(Ud.NZ)-CMAX) 60,60,110
INDEX(NZ»=I
CMAX = U(I.NZ)
CONTINUE
1NDEX1(NZ»= INDEX(NZ) H
1FIINOEXKNZ) .GT. L2I NOSTOP(NZI = 1
.GT.L2I INOEX11NZ) = INDEX(NZ)
.LT. INDEX(NZl) .AND. (INOEX(NZ) .GT. 2)) GO TO 150
,NZ)) GO TO 270
IF(INDEXUNZ)
IFUKLEHHNZ)
11 = INDEX(NZ)
iFICPAST .LT. Udl
iNDEX(NZ)= INDEXHNZI
150 ,*IOEX= INOEX(NZ)-!
Kl* KLEWl(NZ)
00 140 I»K1 ,NIOEX
140 KDES(I,NZI= 0£SKRO»CMAXUM(I ,NZ )**XPONT
INDtX(NZ)
270 CONTINUE
270 iFlNOCLOR .NE. II RETURN
00 280 I"2,L2
R( !)=• A*(CLORIOd + l)-2.*CLORIO(I)»CLORIO(I-ll I-B (I) «( CLORID( I )-
* CLORIO( I-1I)+CLORID( I)
280 CONTINUE
00 250 1=2,L2
250 CLORIOd )•= Rdl
uLORIOd)= CLOR1012)*DENOM
CLORID(LI= CLORID(L2)*OENAM
260 RETURN
END
00018860
00018870
00018880
00013890
00018900
00018910
00018920
00018930
00018940
00018950
00018960
00018970
00018980
00018990
00019000
00019010
00019020
00019030
00019040
00019050
00019060
00019070
00019080
00019090
00019100
00019110
00019120
00019130
00019140
00019150
00019160
00019170
00019180
00019190
00019200
00019210
00019220
00019230
279
-------
SCRAM Program Listing (Continued)
SUBROUTINE SEO
StJIMENT CALCULATION SUBROUTINE, CALCULATES SEDIMENT FLOW
COMMON /SEDATA/SUBI 10,20) ,ADJLI (21 ),ADJLa(20) , RNF14, 20) , INF ( 4, 2 0)
L .SEDRAT, HECT, AK1(101,AK2(10),ST(10)
SUbtK,I)= SUBPLOT DESCRIPTION OF EACH ZONE
AOJLI = INPUT ADJUSTED SEDIMENT LOAD(GM)
ADJLO = OUTPUT ADJUSTED SEDIMENT LOAD1GMI
RNHJ,I) = X RUNOFF TO CORRESPONDING INF ZONE
INMJ,I) = ZONE TO WHICH RUNOFF FROM ZONE I RUNS
SfcORAT= SEDIMENT LOSS RATE(GM/SEC)
HECT= WATERSHED AREA (HECTARES)
COMMON /CONST/ COM 50 ) , I OPT ( 50) , KPEST
EQUIVALENCE
ALLUW FOR MODICATION OF ST(=K3), CONSTANTS FOR SEDI
TO INCLUDE EFFECT OF CANOPY COVER.
VALUE FOR CANOPY COVER IS STORED IN CONI28)—DEFAULT VALUE IS 0.9
RATIO= (DATMAT-TOLD )/ (DATMAT-DATPL)
ALF= CONI28I- RATIO *CON(28)
IF« TOLD .LT.DATPL) .OR. (TOLD .GT.DATHAR I) ALF = 0.0
1F(( TOLD .GE.DATMAT).OR.{ TOLD .LE. DATHAR)) ALF= CONI28)
DO 85 1=1,10
85 ST(I)= ST(I )*(1.0-ALF)
FOLLOWING IS FOR CHANGING AK1(I»
DETtRMINE # OF MONTHS SINCE PLANTING! TOLD,ETC. ARE IN DPSEC)
tMO= (TOLD-DATPL) /<60.*60.*24.*30. (
L>0 95 1=1,10
4K1(1)= 10.<• 3tiO.» EXP(-EMO)
IF (EMO.GT.6.) AKKI) = 10.
00019240
00019250
00019260
00019270
00019280
00019290
00019300
00019310
00019320
00019330
00019340
00019350
00019360
00019370
00019380
00019390
00019400
00019410
00019420
00019430
00019440
00019450
00019460
00019470
00019480
00019490
00019500
00019510
00019520
00019530
00019540
00019550
00019560
00019570
00019580
00019590
00019600
00019610
00019620
00019630
00019640
00019650
00019660
00019670
00019680
00019690
00019700
00019710
00019720
00019730
00019740
00019750
00019760
00019770
280
-------
SCRAM Program Listing (Continued)
95 CONTINUE 00019780
C rfRITE (6,99) EMO. AK1 00019790
99 FORMAT! • SEDI' , 5X, 11E10.3I 00019800
00 10 N=1,NZN 00019810
ROMAX = SUB(10,N)»DT 00019820
AVG = THfcTN(l,N)*SUB(9,N)*AMINl(l..ROMAX) 00019830
Z (WO*AVG*SUB<3,N))*»1.5 00019840
ELFB - 0. 00019850
IF (Z.EU.O.) GO TO 9 00019860
ITYP SUBU.N) 00019870
IF (ITYP.GT.10) CALL ERRORI5) 00019880
TCB - AK1(ITYP)»Z 00019890
JCB AK2(ITVPI*Z 00019900
OFI STUTYP)*RAINR(N)**2 00019910
I F(RAINR(N).EO.O. I DFl* STIITYP)*(5.E-4**2) 00019920
ALPH = SUB(4,N)*DCB/TCB 00019930
IHET = SUB(4.N)*DF1/TCB 00019940
IF (ADJLI(N) .EQ. 0.) GO TO 12 00019950
C 00019960
c CALCULATE INITIAL CONSTANT OF INTEGRATION 00019970
C (USING LOAD CARRIED FROM LAST SLOPE BOTTOM) 00019980
C 00019990
tLFB - AOJLI(N)/(SUB(5,N)*DTOLD) 00020000
C DRJP EXCESS SEDIMENT 00020010
IF IELFB .GT. TCB*ALIM) ELFB = TCB*ALIM 00020020
12 C = -ELFB/TCB 00020030
X = l./SUB(7,NJ 00020040
OF(1) = {< ( (l.-THET)/ALPH)*(l.-EXP(-ALPH*XM)+C*EXP(-ALPH*X) ) 00020050
i *DCB 00020060
ELFIU • (X-(OFU»/DCBI)*TCB 00020070
ELF(2) - ELF(U 00020080
IF(SUB(7,NI.EQ.1.J GO TO 6 00020090
1NCR - SUB(7tN) 00020100
C CHtCK DETACHMENT RATE AND LOAD INCR POINTS 00020110
JO 5 K=2,INCR 00020120
OIST = (SUB(4,N)/SUB<7,N))*K 00020130
X = OIST/SUB(4,N) 00020140
UFI2) = 1(1(1.-THETI/ALPH)*ll.-£XP(-ALPH*X»»+C*EXP(-ALPH*X) I 00020150
1 *OCB 00020160
ELFI2) = *EXP(ALPH*X) 00020260
4 CONTINUE 00020270
DFU) = DF(2) 00020280
cLF(l) = ELF(2) 00020290
5 CONTINUE 00020300
6 tLFB = ELF(2J 00020310
281
-------
SCRAM Program Listing (Continued)
c 00020320
C CAL.ULATE OUTPUT ADJUSTED SEDIMENT LQAD(GM) 00020330
c 00020340
9 ADJLO(N) ELFB*SU8t5,N)*OT 00020350
10 CONTINUE 00020360
C RtiTORE STII) 00020370
DO 30 1=1,10 00020380
3u iTIII= T6HPI1) 00020390
RETURN 00020400
cND 00020410
282
-------
SCRAM Program Listing (Continued)
SUBROUTINE SEQDAT
RE«j SEQUENTIAL DATA
HINDI20),
i TEMPI20), RAD120), PRESI20I, HUMC20), RMF120), EMF120I
JATA RTP/20*1.E30/
JATA CNVRTR /10., .01, 2.54. 30.48, 3*0./
JATA CNVRTW /100., 30.48, 44.703, 51.444/
OATA CNVRTS /!., I./
OATA CNVRTP /1013.3, 68.95077
OATA RAIN. DAYS, ANIT / 'R
1 .'D
•N
ISH=1
KTR=0
cTIME=0.0
C PUNCH 500
500 FORMATC ELAPSED TIME(SEC) VS RAIN RATE(CM/SEC) M
KTEPA=0
C REftO HEADER CARD
10 READ (4, 100G,END*50) TYPE.IFLG
1300 FORMAT(A1,9X 512)
SV = 0.
SV1 = 0.
SV2 = 0.
iF (TYPE .NE. RAIN) GO TO 30
c RAINFALL CARDS
rtRITE (6,1004)
1004 FORMAT I'1 RAINFALL HISTORY1//' YEAR MONTH DAY HOUR
1QND RAINICM/SEC)'//)
THIS NZN IS THE SAME AS THE ONE CALC.
00020420
00020430
00020440
00020450
00020460
00020470
00020480
00020490
00020500
00020510
00020520
00020530
00020540
00020550
00020560
00020570
00020580
00020590
00020600
00020610
00020620
00020630
00020640
00020650
00020660
00020670
00020680
00020690
00020700
00020710
00020720
00020730
00020740
00020750
MINUTE SEC00020760
00020770
00020780
00020790
IN S.R. INPUT
WE HAVE TO READ IT HERE BECAUSE S.R. INPUT IS CALLED AFTER S.R.SEQDAT00020800
00020810
REA014.5) NZN
FORMATII5)
: RMFtI) IS ARRAY OF MULTIPLYING FACTORS
C IF RMF(l) EQ -I. . READ A SET OF RAIN CARDS FOR EACH TIME
I
KEADI4, 10101 (RMF(I), 1=1, NZN)
1010 FQRMATI20F4.0)
iF(RMF
-------
SCRAM Program Listing (Continued)
68 JO 61 1=1,NZN
MEAD (4, 1001,END=22) D.RAINRd)
IF(Dd).Eq.O.) GO TO 200
61 CONTINUE
1001 FORMAT IF*. 0, 5 UAF2.0) tlXF 12.0)
200 CONTINUE
CALL DATEINI D.SEC)
SV = 0(1)
SV1 - Dl 2)
SV2 D( 3)
; CONVERT UNITS If NECESSARY
iF( IFLG(l).EQ.O) GO TO 301
DO 45 1=1,NZN
4s RAINR(I)= RAINRd) *CNVRTR(IFLG<1))
301 CONTINUE
: READ NEXT DATA CARD TO DETERMINE RATE
14 GO T0( 15,25). ISM
15 DO 62 1=1,NZN
*EAD (4,100l,END=21) DS.RANLII )
IFIDSl1).EQ.O.) GO TO 300
62 CONTINUE
uO TO 300
25 READ(4,1001,END=21)DS,RANLdl
DO 47 1=1,NZN
47 RANLd) = RANLdl * RMF(I)
300 CONTINUE
C CHfcCK FOR END
JO 16 1=1,6
IF IDSUI .NE. 0.) GO TO 17
16 CONTINUE
GO TO 20
17 IF (DS(1J .EO. 0.) OS(l) = SV
IF (DS(2) .EO. O.I DS(2) = SV1
IF (DS(3) .EQ. O.J OS(3) = SV2
CALL DATEIN (DS.SEC2)
IF(SEC.EO.SEC2I GO TO 22
: CONVERT UNITS
IFdFLGl D.EQ.O) GO TO 201
U0446 1=1,NZN
44o RANL(I)= RANLdl*CNVRTRdFLGdl)
201 CONTINUE
: DETERMINE RATE
DO 90 1=1,NZN
RAINRT(I)= (RANL(I)- RAINRII))/ ISEC2-SECI
IF(RAINRTd) .GE. O.I GO TO 90
IF(RANLd) .NE. 0.) CALL ERROR(3)
RAINRTd ) = 0.
90 CONTINUE
175 SV = DS(l)
iVl = DS«2)
SV2 » DS13)
00 100 1 = 1,NZN
IF (RTPd l.NE.RAINRTd )) GO TO 101
100 CONTINUE
00020960
00020970
00020980
00020990
00021000
00021010
00021020
00021030
00021040
00021050
00021060
00021070
00021080
00021090
00021100
00021110
00021120
00021130
00021140
00021150
00021160
00021170
00021180
00021190
00021200
00021210
00021220
00021230
00021240
00021250
00021260
00021270
00021280
00021290
00021300
00021310
00021320
00021330
00021340
00021350
00021360
00021370
00021380
00021390
00021400
00021410
00021420
00021430
00021440
00021450
00021460
00021470
00021480
00021490
284
-------
SCRAM Program Listing (Continued)
uO TO 18
101 CONTINUE
c OUTPUT
*RITE = 0.
222 CONTINUE
liO TO 175
C DAY OR NITE EPA DATA CARDS
C (ONLY DAY FUNCTIONING NOW)
30 IF(TYPE.NE.DAYS .AND. TYPE.NE.ANIT) CALL ERROR(3)
MRITEI6, 1006)
1006 FJRMATC1 EPA ENVIRONMENTAL DATA1// • YEAR MONTH DAY HOUR
ll/TE SECOND HIND V TEMPERATURE SOLAR RADIATION ATMOS
2ES RELATIVE HUMIDITY1//)
ftEAD(4,10101 (EMFil 1,1 = 1, NZNI
32 CONTINUE
1F(EMF(1l.EO.-l.) GO TO 52
READ (4,1002iEND*41) D, W INO(1 I,TEMP(1),RAD(1),PRES(II ,HUM(11
JO 66 1=2,NZN
HINDU) *EMF(I)
EMF(I)
RAD (1) * EMF(I)
PRESI1) * EMF(I)
HUM(1) * EMF(I)
-------
SCRAM Program Listing (Continued)
6* CONTINUE
130WERT UNITS AS NECESSARY
00 65 1=1,NZN
IFUFLGCll .NE. 0» HINDU) = W INO( I) *CNVRTH( I FLG ( 11 )
IF (IFLGI2) .NE. 01 TEMP(I) 0.5555556*(TEMPI I)-32.J
IF(IFLG(3t .NE. 0) RAD(I) = RAD(I)*CNVRTS(IFLG(3 I I
IF (IFLGI4J .NE. 0) PRES(I) PRES(I)*CNVRTP(IFLGI4))
IF (IFLG15) .NE. 0» HUM(I) = HUMUI/100.
• RITE (6.10051 D, WIND!I).TEMPI I 1,RADII).PRES(I),HUM!I I
65 CONTINUE
CALL DATE1N (O.SEC)
SV = D(l)
SV1 = D( 2)
SV2 = D( 3)
ICTEPA = KTEPA + 1
*
-------
SCRAM Program Listing (Continued)
SUBROUTINE SETUP
C
UIMENSIUN D12) ,T12)
JIMENSION CARDI20)
C
CALL TODAY1D)
CALL TIMEODU)
dR ITE1 6,4) 0, T
8 rfRITE 16,1)
rfR ITE1 6, 2 )
JRITE16.3J
MRITE(6.4)0,T
rfRITE (6,1)
HRITE16.2)
WRITE16.3)
URITE (6,1010)
10 READ(4,1005,ENO=20) CARD
HRITE16, 1006) CARD
uO TO 10
20 REWIND 4
HRITEI6, 10111
30 READ15,1005,END-40) CARD
HRITE16, 1006) CARD
GO TO 30
40 REWIND 5
RETURN
4 FORM ATI' l'/15X'DATE:« ,2A4,65X« TI ME : • ,2A4I
1 FORMATl //56X17I • EM , /51X271 • E M /47X341 • E M /44X401 • E M /
141X35('EM.1X,9('SM/
-------
SCRAM Program Listing (Continued)
B FORMAT(15X20A4)
1011 FORMAT!'1',40X'1NPUT
END
•
t
f
t
1
t
ff
t
f
r
IX,
IX,
IX,
IX,
IX,
IX,
IX,
IX,
IX,
IX.
lb(
16!
Ibi
161
161
15!
14(
13!
10!
Ill
10!
•S*
•S'
•S'
•S'
•S1
•S'
•S'
•S'
•S'
•S'
•S'
,1X6!'S'l,6X 41'S'>,1X7!'L'1,9X14!'L')/
, 1X71 'SM ,4X 5CS' 1,1X7 ( •L•),9X141•L•)/
1X.30CL')/
1X,30( 'LM/
1X,30( 'L')/
1X,30('L')/
1X,29('L')/
1X,29('L'l/
IX,281 'L'l
1X,28( 'LM/
IX.29I'L')/
1X,30('L'l/
1X.3K 'L')/
1X,32('L'l/
IX,351'L')/
L'1/50X281'L'1/55X161'L')I
SEQUENTIAL DATA CARDS'//)
NAMELIST DATA CARDS'//!
00022920
00022930
00022940
00022950
00022960
00022970
00022980
00022990
00023000
00023010
00023020
00023030
00023040
00023050
00023060
00023070
00023080
00023090
00023100
00023110
00023120
00023130
288
-------
SCRAM Program Listing (Continued)
iUBROUTINE SIMPSN (SUM.IS.NZ) 00023140
C 00023150
COMMON /ADOATA/ 0127,20), 5(27,20), KNT, SSSC27.20I 00023160
i ,00127), VtL(27), THETJC27), B<27>,KDES(27,20),CMAXUMI27,20),00023170
i THETX.XMAX, H, KTIME.II, A, DENOM.DENAM,INDEX!20),INDEXl(20),00023180
3 ANT, AX, IISAVE, IGOR, NVAL(20),DESKRO, XPONT.KLEWH 20) ,DVST , 00023190
-------
SCRAM Program Listing (Continued)
FUNCTION SOLAR IA.T)
>
-------
SCRAM Program Listing (Continued)
SUBROUTINE VOLT
SUBROUTINE TO PREDICT PESTICIDE LOSS DUE TO THE PESTICIDES'
VOLATILE PROPERTIES
COMMON /EVAP1N/ ELE2,WVL2,TEM2
COMMON /TIMES/ TOLD.TNEW,DT.OTOLD,TOUT,TSTRT,TSTOP,TRA1N,PIN,
1 EPATM, PK1NI13), PROGDTI3), PESTM
KEAL*8 TOLD, T NEW, DT.DTOLD, TOUT, TSTRT, TSTOP, TRAIN, PIN, EPATM, PESTM
;OMMON /KATERD/ NZN, RAINR, THETAl 27 ,20 I ,THETN(27, 20)
COMMON /SEDATA/SUB(10,20)
COMMON /VOLTD/ ENt,, ALFA.DVI 27 , 20 ) , 01 ST ( 27,20) , IV I ,PPB ( 27,20) .
1 DVS(27,20),P2
DIMENSION XRYS127.20) ,XRYI(27,20 I,1C(20) ,CZ(20),KFLAG(20), TP(27) ,
I Fl(27,20),F2(27,20).FFI27.20)
(27.20)
(NL.NZ) = NL—LAYER
NZ—ZONE
ENL,= NANOGRAMS OF PESTICIDE APPLIED I INPUT)
ALhft = APPLICATION RATE OF PESTICIDE (LBS/ACRE) (INPUT!
DiiT (27,20)= DISTRIBUTION OF PESTICIDE (INPUTI
DVI27,20)= DIFFUSION COEFFICIENTS (INPUT)
DVii27,20)= DIFFUSION COEFFICIENTS ( = DV IF DV NE 0.; OTHERWISE CALC)
VTIME = PREVIOUS ELAPSED TIME SINCE PESTM (DATE OF PEST. APPLICATION)
VII = PRESENTS ELAPSED TIME SINCE PESTM (DATE OF PEST. APPLICATION)
VT= DT.TIME INCREMENT
IVl» FLAG FOR 1ST TIME THRU VOLT
P2= AMT. OF PESTICIDE REMAINING W/R TOTAL
8£>=SUBJ6,I )=BULK DENSITY OF SOIL IG/CC)
NL*SUB(8.I) = NO. OF LAYERS IN ZONE! I )
DX = SUB(9,I)= LAYER THICKNESS (CM)
TEKP»TEM2= TEMPERATURE IN 0-1 CM (DEG. C)
IC(MZ»= LAYER NO. FOR THIS ZONE, ALL ABOVE IT HAVE EQUAL CONC.
PPB127.20I* CONC. OF PESTICIDE IN PARTS/BILLION
(NEITHER IN SOLN. NOR ADSORBED)
KFLAG(NZ)=1 , CHANGE CZINZI NEXT TIME AROUND
1F(TOLD.LT. PESTM) RETURN
CHtuK FOR 1ST TIME THRU
iF(IVl.NE.O) GO TO 90
Sfcl UP FOR 1ST TIME THRU
V/TIME= 0.0
00023570
000235SO
00023590
OOC23600
00023610
00023620
00023630
00023640
00023650
00023660
00023670
00023680
00023690
00023700
00023710
00023720
00023730
00023740
00023750
00023760
00023770
00023780
00023790
00023800
00023810
00023820
00023830
00023840
00023850
00023860
00023870
00023880
00023890
00023900
00023910
00023920
00023930
00023940
00023950
00023960
00023970
00023980
00023990
00024000
00024010
00024020
00024030
00024040
00024050
00024060
00024070
00024080
00024090
00024100
291
-------
SCRAM Program Listing (Continued)
12= VTIME
TQTP= ALFA* ENG
PTOTAL=0.
00 11J J=l,NiN
60= SUB16.J)
NL- SUB(8tJ)-l
DO 13 1=1, NL
XRYSUtJ» = DISTd.J) * TOTP/NZN
t>TOTAL = PTOTAL* XRYSII.J)
XRYIU.J) = XRYSIIiJ)
U PPBI I , JI=XRYI( I, JJ/BD
KFLAGI J)=0
IC(J)=1
uZ(J) = XRYI( l.J)
F2( I tJ 1= 0.0
113 CONTINUE
P2=PTOTAL/TOTP * 100.
PKluT INITI AL VALUES
*RITE16.2000) ENG, ALFA
2000 FORMATC1'. 'INITIAL CONDITION OUTPUT" //
1 lX,Gl2.4t2Xt "NANOGRAMS OF PESTICIDE APPLIED1 /
1 IX. G12. 4, 2X, -APPLICATION RATE 1 LBS/ ACRE I • //)
LALL VPRNT
IV 1=1
90 VTT= TOLD-PESTM
CHcCK TO SEE IF ELAPSED TIME SINCE LAST CALC. IS GE 1 HOUR
IFIVTT-VTIHE .LT.3600.) RETURN
PKJCEED WITH CALCULATION
T 1= T2
T2= VTT
VTIME=VTT
VT=T2-T1
PTOTAL=0.
00 501 JJ=1,NZN
NL=SUB(8,JJ)-1
dO=SUB(6,JJl
NLL-NL-1
OX=SUB(9,JJ)
00 5011 l=l,NL
F1II,JJ)= F2( I.JJ)
TP(II= TEMPI - (1-1) *0.5
lFtTEMPI.LT.35.) TP(I)= TEMPI
IF(OV(ItJJI.EO.O.IGO TO 5012
OVS( I. JJ)= DVl I.JJ)
GO TO 5011
5012 TH1= THETN(I+l.JJ) *100.
TH2= THI*TH1
00024110
00024120
00024130
00024140
00024150
00024160
00024170
00024180
00024190
00024200
00024210
00024220
00024230
00024240
00024250
00024260
00024270
00024280
00024290
00024300
00024310
00024320
00024330
00024340
00024350
00024360
00024370
00024380
00024390
00024400
00024410
00024420
00024430
00024440
00024450
00024460
00024470
00024480
00024490
00024500
00024510
00024520
00024530
00024540
00024550
00024560
00024570
00024580
00024590
00024600
00024610
00024620
00024630
00024640
292
-------
SCRAM Program Listing (Continued)
TH3=TH2*TH1
TH4=TH3*TH1
FH5= TH4*TH1
TH6=TH5*TH1
FEMP= TP (I )
TEMP2= TEMP*TEMP
TEMP3= TEMP2*TEMP
JVS(I,JJ) = 10.** ( -0.313-1.051 * TH1
1 -8.997 * 60 + 6.021E-5 * TH1 * TEMP2
00024650
0002*660
00024670
00024680
00024690
00024700
00024710
0.054 * TH2 -8.494E-4 *TH300024720
7.359E-7 * TH1* TEMP3 00024730
c +1.483E-6 * TH4 * TEMP -8.863E-8* TH5 * TEMP + 1.362E-9 * TH6* 00024740
3 TEMP + 1.588 * TH1 * BO -0.108 * TH2 * BD + 2.880E-3 * TH3 * BO 00024750
4 - 2.560E-5 * TH4 * BD + 4.664E-2 * TEMP * 80 - 3.013E-3 * TH1 *
b TEMP * BO J
5011 CONTINUE
ICC= IC(JJ)
IF(KFLAG( JJ).
-------
SCRAM Program Listing (Continued)
uO TO 501
200 :ONTINUE
kc«DY TO CHANGE CZ
-------
SCRAM Program Listing (Continued)
SUBROUTINE VPRM
C
C SUBROUTINE TO PRIM VALUES GENERATED BY SR VOLT
C
COMMON /SEOATA/SUB110, 20)
C
COMMON /TIMES/ TOLD. TNEW , DT .DTOLD, TOUT , TSTRT , TST™ , TR AI N,P I N,
1 EPATM, PRINT13), PROGDTI3), PESTM
r
*EAL*8 VLAP
C NTH =MAX VALUES OF THETA (NO. OF LAYERS!
p
iF(TOLD.LT.PESTM) RETURN
VLAP=TOLO-PESTM
WRITEI6,20001
2000 FORMAT( 5X,•VOLITALIZATION OUTPUT1 )
IFUV1.EQ.-1I GO TO 10
CALL OATOUT(TNEH,D,0)
WRITE(6,2001) VLAP
2001 FORMATI5X,'ELAPSED TIME: ' ,G12.4,ISECt
10 WRITEI6,1000)
1000 FORHATCO', SOX'ZONE DEPTH PROFILE1)
SUB(8,I)
TH= 1.0
L)0 40 1=1, NZN
40 TH = MAX I(TH,SUB I 8.1) )
NTH=TH - 1
ISW=1
NA*1
NB=NZN
IF(NB.LE.IO) GO TO 50
NB=10
50 CONTINUE
WRITE (6,310)
31U FORMATC O1 ,55X,'ZONE #' )
WRITE (6,3201 (I1,I1=NA,NB)
320 FORMATI • PROFILE1,3X, IOI5X,12,4X1 I
«RITE<6,321)
321 FORMAT!1 VOLITALIZED PESTICIDE (PPB) •)
DO 340 12=1,NTH
340 WRITE (6,330) 12,(I PPB(I2,I1I. I1=NA,NBI
330 FORHATUX, 12,4X, 10F11.3 I
IFUV1.EC.-1) GO TO 52
WRITE(6,431)
431 FORMAT('ODIFFLSION COEFFICIENTS1)
JO 440 12=1,NTH
440 WRITEI6.33 J 12, ( ( OVS ( 12 , 11) , I1»NA ,NBI )
00025510
00025520
00025530
00025540
00025550
00025560
00025570
00025580
00025590
00025600
00025610
00025620
00025630
00025640
00025650
00025660
00025670
00025680
00025690
00025700
00025710
00025720
00025730
00025740
00025750
00025760
00025770
00025780
00025790
00025800
00025810
00025820
00025830
00025840
00025850
00025860
00025870
00025880
00025890
00025900
00025910
00025920
00025930
00025940
00025950
00025960
00025970
00025980
00025990
00026000
00026010
00026020
00026030
00026040
295
-------
SCRAM Program Listing (Continued)
33 FORMATI3X, 12,4X, 10GU.3 )
uO TO (51.52), ISW
51 iFtNZN.LE.10) GO TO 52
NA= NB + 1
1X8= NZN
ISW = 2
t,0 TO 50
52 CONTINUE
*RITE(6,70) P2
70 FORMATCO X PESTICIDE REMAINING W/R TOTAL', F10.3)
RETURN
END
00026050
00026060
00026070
00026080
00026090
00026100
00026110
00026120
00026130
00026140
00026150
00026160
296
-------
SCRAM Program Listing (Continued)
bUBROUTINE hATER(NZ.NEWFLG) 00026170
00026180
SUBROUTINE TO PREDICT THE AMT. OF RUNOFF ON THE WATERSHED DURING 00026190
tnCH EVENT, ANU THE MOVEMENT OF WATER INTO THE SOIL PROFILE DURING 00026200
A«D AFTEK AN EVENT. 00026210
00026220
COMMON /SfcOATA/SUB(10,20),AOJLI(21),ADJLOC20I,RNFI4.20I,INF<4,20) 00026230
00026240
COMMON /WATERD/ NZN, RAINR120I, THETA ( 27 ,201 , THETN 127 ,20) ,CUMRO 00026250
i ,CUMFLT,DHTAB(50,4,10) ,NUMDH(10),RINF(20 I,CIT(20),VELC(27, 20100026260
,0(27,20).SUMRN,WATROT,SUMIN.RCR
COMMON /TIMES/ TOLD,TNEW.DT.DTOLD,TOUT,TSTRT.TSTOP,TRAIN,PIN,
I EPATM, PRINTU), PROGDT{3)
*EAL*8 TOLD, TNEW.OT.DTOLD,TCUT, TSTRT.TSTOP, TRAIN, PIN, EPATM
UIMENSION
RHS(27), CAPI27), COEFC77)
HH(27I, WORK(27),
NZN= NO. OF ZONES
RA1NR= RAINFALL RATE (CM/SEC)
THETA = WATER PROFILE AT PREVIOUS CYCLE
THLTN= NEW WATER PROFILE
CUMKO= CUMULATIVE RUNOFF AT BOTTOM IZONE # 21)
CUMFLT= CUMULATIVE INFILTRATION LOSS
OHTAB = THETA, DIFFUSIVITY, PRESSURE HEAD TABLES
NUMDH= NO. OF ENTRIES IN CORRESPONDING DHTAB
RINF= INFILTRATION RATE
VELC» INFILTRATION VELOCITY
Q= INFILTRATION FLUX
HI ZONE NUMBER (SUPPLIED THRU CALL I
kti = SOIL TYPE (= SUB(1,NZ) IN COMMON /SEDATA/
(i =LAYER THICKNESS =SU8(9,NZ)
NDH= # OF VALUES OF OHTAB, MAX=50
= NUMDH(NS)
UEND1 = SUB<8,NZ)
fHl= THETAi l.NZ)
HH(NI = PRESSURE HEAD AAT LAYER N
THETA(N.NZ) = MOISTURE (PERCENT) AT LAYER N
WHERE N=l IS THE RAIN LAYER, N=2 IS THE TOP SOIL LAYER
NZ IS THE ZONE NUMBER
NENO(NZ) = NENDl - NUMBER OF SOIL LAYERS
NENDP =NEND1-1
COMPUTE PRESSURE HEAD VALUES (HM) FROM TABLE FOR THETA
00026270
00026280
00026290
00026300
00026310
00026320
00026330
00026340
00026350
00026360
00026370
00026380
00026390
00026400
00026410
00026420
00026430
00026440
00026450
00026460
00026470
00026480
00026490
00026500
00026510
00026520
00026530
00026540
00026550
00026560
00026570
00026580
00026590
00026600
00026610
00026620
00026630
00026640
00026650
00026660
00026670
00026680
00026690
00026700
297
-------
SCRAM Program Listing (Continued)
C VALUES VIA INTERPOLATION. ITABLE COMPUTES CORRECT ENTRY 00026710
C POINTS INTO TABLE FOR INTERPOLATION 00026720
C 00026730
C 00026740
C 00026750
C THcTAU.NZI IS OLD VALUES OF THETA 00026760
C THETNU.NZI IS NEW VALUES OF THETA 00026770
C WIN) IS THE WORKING ARRAY AND IS = THETAU.NZ) AT BEGINNING OF ROUT IN00026780
C 00026790
HH(1) = 0. 00026800
*(!>= THETA(l.NZ) 00026810
00 50 N=2.NEN01 00026820
ri(N) = THETA1N.NZ) 00026830
1 = ITABLEIM(N) tOHTABIltl>NS).NOH-1) 00026840
r!H(N) = DHTABI1,3,NS) * (WIN) - DHTABI1 , l.NS)) / 00026850
1 IDHTAB(I*l,l,NS) - DHTABI I , l.NS) ) » (DHTAB( I + 1.3.NS) 00026860
i - OHTABU.3.NSI I 00026870
50 CONTINUE 00026880
THETA(NENOl+l,NZ)= THETAINEND1,NZ) 00026890
WJNENDH-1I = THETA(NEND1 ,NZ) 00026900
C 00026910
C iETS BOUNDARY CONDITION AT EQUAL MOISTURE CONTENT LAYER 00026920
C 00026930
HH(NEND1+1) = HH(NEN01I 00026940
C 00026950
C DOES CALCULATED INFILTRATION EXCEED RAINFALL RATE? 00026960
C 00026970
22 CONTINUE 00026980
C 00026990
C L>OES RAINFALL EXCEED THETA SATURATION? 00027000
C 00027010
25 THETA(l.NZ) = TH1 * RAINR(NZ)* DT/G 00027020
W(2I = THETA(2,NZ) * THETA(l.NZ) 00027030
C 00027040
C UatS W(2) EXCEED THETA SAT? 00027050
C 00027060
IFIVM2I- OHTA6(NOH,lfNS) ) 27,27,30 00027070
27 RINFINZ) THETAI1,NZ)*G/OT 00027080
*UNOF=0. 00027090
GO TO 60 00027100
30 RUNOF = K(2J - DHTAB(NDH,1,NS) 00027110
RINF(NZI = (THETAI l.NZI - RUNOFXG/DT 00027120
*12I >=OHTAB(NDH,1 ,NS) 00027130
60 1= ITABLEIW12) ,DHTAB(1,1,NS).NDH-1) 00027140
C 00027150
C DETERMINE NEW HH(2) 00027160
C 00027170
rtH(2»= OHTABII,3,NSt * (W(2) -DHTABII,1,NSJI 00027180
1 /(DHTAB(H-l.l.NS) -DHTAB (I , 1 ,NS) )*< DHTABI 1 + 1, 3, NS ) -DHT AB( I ,3,NS) ) 00027190
C 00027200
C SET UPPER BOUNDARY CONDITION 00027210
C 00027220
62 Ml1I=HI2) 00027230
HH111 = HH(2) 00027240
298
-------
SCRAM Program Listing (Continued)
C CALi-JLATE CONDUCTIVITY FOR EACHDEPTH LEVEL
C WOK« 11 - K-U ) = K + ( 1-1)
C j= ITABLEIHI1) .OHTABI1,I,NS),NOH)
C C 1 = DHTA8U,4,NS) + IWI1) -DHTABIJ,1,NSI)/(DHTABl J+1, I, NS)
C 1 -OHTABIJ.l.NS)l*- DHTAB(I,1,NSI)/(DHTABI1*1,3,NS)-
1 DHTABI1.3.NS) »
65 t ITAtiLtl W(N*1) , DHTA8( 1,1 ,NS ) , NDH-l)
CX = DHTABI 1 ,4,NS1* IW(N*l) -OHTABtI,1,NS)•/(DHTAS(1+1,1,NS»
I -OHTABII,l.NSI) *(DHTAB(1*1,4,NS) -OHTABII.4.NS))
«IORK(N)= OHTA8(I,2,NS) *CAP(N)
IF(ABSIWIN) -WIN*1) j-l.E-6 ) 90,90,70
70 DIP = (C 1-CXI/lHHJN) - HHIN + 1))
^ORK(N) = DIF
90 J = I
Cl = CX
1FIN.EQ. 1) CON1= OIF
200 CONTINUE
•ORK(NENDl) =0.
MORKdl = 0.
C SET OP COEFFICIENT MATRIX AND RHS
105 M = 3
OTDXS= DT/(G*G)
Cl= OTDXS*WORK(1I/CAPI1)
CX = OTOXS*WORK12)/CAPI1)
C3 = Cl+CX
C HATRIX ELEMENT TOO LARGE
C
IF (ABS(C3) .GE. 2. .AND. NEWFLG .EQ. 01 GO TO 810
COEF(l) 2.*C3
COEFI2) - -CX
KHSI2I (2.-C3l*HHI2l + CX*HH(3)
DO 110 N = 2.NENDP
Cl = OTOXS*MORKIN)/CAP(NI
CX = DTOXS*WORK(N+1)/CAP(NI
C3 = Cl * CX
IF (ABSIC3) .GE. 2. .AND. NEWFLG .EQ. 0) GO TO 810
COEF (M) -Cl
COEFIM + 1) 2. «• C3
COEF(M*2) -CX
RHSIN+U C1*HH(NI + ( 2 .-C3) *HH (N + l I * CX*HH(N+2)
I + 2.*li * IC1-CX)
M = M * 3
110 CONTINUE
C SOLVE - NEW HH WILL BE IN RHS
C
C INVERT TR10IAGONAL MATRIX
C
CALL GELB(RHSt2).COEF , NENDP, 1,1.1. l.E-5, IER)
00027250
00027260
00027270
00027280
00027290
00027300
00027310
00027320
00027330
00027340
00027350
00027360
00027370
00027380
00027390
00027400
00027410
00027420
00027430
00027440
00027450
00027460
00027470
00027480
00027490
00027500
00027510
00027520
00027530
00027540
00027550
00027560
00027570
00027580
00027590
00027600
00027610
00027620
00027630
00027640
00027650
00027660
00027670
00027680
00027690
00027700
00027710
00027720
00027730
00027740
00027750
00027760
00027770
00027780
299
-------
SCRAM Program Listing (Continued)
iF HER) 400,115,400
UD CONTINUE
_01PUTE NEW THETAS AND CUMUNICATIVE INFILTRATION
JO 410 N= 2.NEN01
TERM = (RHS(N) - HHJN)I*CAP(N-1I
THETN1N.NZJ = WIN) * TERM
410 CONTINUE
THETNI l,NZ)= RUNOF
42o CIT(NZ)= CITINZ) * RINFINZI *DT
ACCUMULATE WATER LOSS DUE TO INFILTRATION
LUMFLT CUMFLT t ITHETNINEN01,NZ)-WINENOII)*G*SU3l2, NZ)/1000.
iUHl = 0.
SUH2 = 0.
DO 425 I=2,NEND1
SUM1 = SUM1 * THETNII.NZ)
->UM2 = SUM2 * H(I )
423 CONTINUE
OIF = SUM1 - SUM2
THETNI2.NZJ THETNI2.NZ) - DIF
THETNINENDl.NZ) W(NENDl)
CALCULATE INFILTRATION VELOCITY-VELC
CALCULATE INFILTRATION FLUX-Q
RHSIII + 2.*G - HHU + 1) - RHS
VELCI l.NZJ = RINFINZI
J( l.NZ) = RINF(NZ)*DT
00 440 I=2,NENDP
VELCd.NZI = UHH(l) T
i. ( 2.*G) )*HORK( 1 »
440 fld.NZ) = THETAd.NZI + Q(l-l.NZ) - THETNCI.NZI
VELC(NEND1,NZ) * (THETNI NEN01 , NZ ) - W( NEN01) ) *G/DT
4INEN01.NZI = Q(NENDP.NZ)
uO TO 900
40o rfRITEC6,9000J JER
oO TO 115
81u UT =1.9*OT /A8SIC3)
U0 TO 22
900 CONTINUE
NEWFLG = I
KETURN
9000 FORMAT ('OGELB ROUTINE ERROR CODE ',121
tND
00027790
00027800
00027810
00027820
00027830
00027840
00027850
00027860
00027870
00027880
00027890
00027900
00027910
00027920
00027930
00027940
00027950
00027960
00027970
00027980
00027990
00028000
00028010
00028020
00028030
00028040
00028050
00028060
00028070
00028080
00028090
00028100
00028110
00028120
00028130
00028140
00028150
00028160
00028170
00028180
00028190
00028200
00028210
00028220
300
-------
APPENDIX C
SCRAM SAMPLE OUTPUT
301
-------
INPUT SEQUENTIAL DATA CARDS
CO
O
to
07
07
07
07
07
07
07
07
SAIN
10
1. 1.
1973 06
1973 06
1973 06
i973 06
1973 06
1973 06
1973 06
1973 06
1973 07
1973
1973
1973
1973 07
1973 07
1973 07
1973
1973
1973
1973
1973
1973 07
1973 07
'.973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1.973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1973 07
1.
28 03
28 03
28 16
28 16
28 16
28 16
28 16
28 16
08 16
08 16
08 17
08 17
06 17
08 17
08 17
08 17
08 17
08 17
08 17
08 17
08 17
08 17
08 18
08 18
08 18
16 20
16 20
16 20
16 20
16 20
16 20
16 20
16 20
16 21
16 21
16 21
16 21
16 21
17 10
17 10
17 10
17 10
17 11
17 11
17 11
17 11
17 11
17 11
17 11
25 ZO
25 21
25 21
25 21
25 21
25 22
30 19
1 .
35
40
30
35
40
45
50
51
52
55
00
02
05
10
15
20
25
30
35
40
45
55
10
20
23
35
40
45
48
50
53 •
55
58
00
05
10
15
20
41
46
51
56
01
06
11
16
21
26
31
10
00
20
30
50
10
15
1.
1.
0.0
0.41
0.0
0.25
0.30
0.34
0.38
0.0
0.0
0.16
0.34
0.41
0.52
0.68
0.80
0.91
1.03
1. 14
1.26
1.37
1.42
1.49
1.60
1.68
1.73
0.0
0.09
0.23
0.33
0.39
0.49
0.56
0.66
0.68
0.73
0.79
0.84
0.89
0.0
0.22
0.44
0.61
0.63
0.65
0.68
0.70
0.72
0.74
0.76
0.0
0.13
0.18
0.25
0.32
0.38
0.0
1. 1.
1.
0
1
D
1
6
11
16
17
0
3
8
10
13
18
23
28
33
38
43
43
53
63
78
88
91
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
6
11
16
21
26
31
0
0
0
0
0
0
0
0
0.0
29.95
0.0
84.27
2600.72
886.04
103.07
38.90
0.0
1760.52
4 62.07
3371.68
2464.52
1313.46
1647.32
5349.21
4184.10
3185.24
2229.42
27.14
26.33
24.74
22.44
20.97
16.87
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
95.31
3 35.31
1648.12
720.39
191.24
9.71
6.46
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-------
1973 07 30 19 18 0.13 0 0.0
1973 07 30 19 19 0.23 0 0.0
1973 07 30 19 20 0.33 0 0.0
1973 07 30 19 25 1.31 5 14932.17
1973 07 30 19 30 2.29 10 22860.34
1973 07 30 19 35 2.52 15 18552.59
1973 07 30 19 40 2.67 20 9359.71
1973 07 30 19 45 2.79 25 3651.77
1973 08 01 17 58 0.0 3 0.0
DAYS 41 1
1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1973 01 01 2.01 83.35 5. 1. .6
1973 07 01 1.94 80. 5. 1. .65
1973 07 08 1.96 77.26 5. 1. .82
1973 07 17 1.72 79.62 5. 1. .80
1973 07 30 1.57 81.46 5. 1. .8
1973 09 01 5.7 75.4 5.5 1. .75
1975 01 01 2.01 83.35 5. 1. .6
CO
O
00
O
O
3
rt
H-
n>
-------
INPUT NAMELIST DATA CARDS
CO
O
CPESTI
PLOTN"= 'P-01'
PESTN!k1=1DIPHENA1IO'
STARTM=73,07,08, 16,52
ENr>T^1 = 73,07,16,21,30
CRPPOT= 0,73,6,13,73,11
of SPiT=5*G. , 73 , 6 , 13 ,
THE TA =
0., .500, .061,
0., .503 ,.061 ,
0. , .500 , .061 ,
0. ,.500, .061,
0.,. 503, .061,
0. , . 500, .061 ,
0...500, -Oil,
0., .500, .361 ,
0. , .500, .061,
0. , .500, .061 ,
DHABAY=2,?3 ,
.05, . 07, . 09, .
.39, .41, .43,.
.68E-5, .86E-5
.56c-3, .80i-3
. 15F- 1, .195-1
.062,.
. 062,.
.062, .
.062, .
. 062 ,.
.062, .
.062, .
. 3 62 , .
.062, .
.062, .
11, .13
45, .47
063,
363,
063,
063,
063 ,
063 ,
063,
063,
063,
063,
,. 15
,.49
,.13E-4,.2
, .12E-
, .26E-
2,.l
1,
-.60F6, -.90E5.-.40E5,-
-.57E3.-.45E3
-.20E2.-.10E2
FUNOFF=
21, 1, 6*0,
,-.33F
,0.0
3,-.
,1,73,
.064,.
.064,.
.064,.
.064, .
.064,.
.064,.
.064, .
.064, .
.064,.
.064,.
,.17,.
,
3E-4,.
7E-2,.
.10E5,
22F.3,-
9,12
065,
365,
065,
065,
365,
065,
065,
065,
065,
065,
19,.
40E-
24E-
,
.066, .
.366,.
.066,.
.066,.
. 366,.
.066,.
.066,.
.066,.
.066, .
.066, .
21 ,.23
067,
367,
067,
067,
067,
067,
067,
367,
067,
067,
,.25
4, .68F-4, .
2, .32"=
-2, .
-. 70E4,-.47E4,
.105
3, -.90
E2,-
.068
.068
.068
.06B
.068
.068
.068
.068
.068
. J68
,.27
12E-
441=-
-.23
.77 =
,.069, 4*.
, .069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.069, 4*.
,.29, .31,.
3, . 18E-3,.
2, .60E-2, .
E4.-.10E4,
2.-.60E2,-
07,
07,
07,
07,
07,
07,
07,
07,
07,
07,
33,
28E
80E
12*0
12*0
12*0
12*0
12*0
12*0
12*0
12*0
'.2*0
12*0
.35, .
. t
. ,
• f
• f
. f
. ,
• »
. ,
• r
• t
37,
-3, .40E-3,
-2, .1
-.80E3,-
.50E2,- .
lE-i,
.68E3,
40E2,
10,1, 10, 2, 4*0,
3, 1, 10, 7, 4«J,
6, 2, '0, 1, 4*0,
7, 2, 10, 1, 4*0,
1, 1, 6*0,
1 , 3 , 10 , 1, 4* 0 ,
5. 1, 10, 4, 4*0,
8, 50, 10, 50, 4*0,
., 1500, 2, 1, 0, 0
1
1
1
1
1
1
1
1
1
.6,
.6,
.6,
. 6,
.6,
.6,
.6,
.6,
.6,
3,
3,
3,
3,
3,
3,
3,
3,
3,
15,
15,
15,
15,
15,
15,
15,
15,
15,
1,
1,
1 ,
1,
1.
A 1
1,
1,
1 ,
1500,
1500,
1500,
1500,
1500,
1500,
1500,
1530,
1500,
2,
2,
2,
2,
2,
2,
2,
2,
2,
1,
1,
1,
1,
1,
1 ,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
0
0
0
0
0
0
0
0
1, 1.47, 4, 425, 187.5,
1, .659, 3, 287.5, 156.25,
1, .496, 3, 300, 106.25,
1, 1.059, 2, 362.5, 175.,
I, .545, 4, 325, 187.5,
1, 0,42, 4, 225, 125,
1, .61, 2, 112.5, 212.5,
1, 0.428, 4, 225, 11.3.75,
AK1=10*200., AK2=10*l.E-2i ST=10*24.,
COM(6)=1.E-5, CQN(7)= .40
CON(9)=l.t CON(10)=1.5, CON(11)=1.6, CON(12)=33.66, CQN(13)=.9,
CCN(14I=1.7, CON(15)=74.00, CON(16)=13, CnN<17)=0., CON(18)= .1,
CON(19)= 10.
IQPTC8) = 0
PR I NT(11=300.,PRINT!2)=3600.,PRINT(31=172800.
IOPT«2l=lt IOPTOI =0,IOPTI4) = 1
!HPT(2(=0,
IOPT(13)=1
O
O
3
n-
p-
(D
-------
O
01
SEND
»«
"AINPALL HISTORY
YC«R MONTH DAY HOUR MINUTE SECOND PAINICM/SEO
3.
3.
16.
16.
16.
16.
16.
16.
17.
17.
17.
17.
17.
17.
17.
17.
17.
17.
17.
17.
18.
18.
18.
20.
20.
20.
20.
20.
20.
35.
40.
30.
35.
50.
52.
55.
0.
7 .
5.
10.
15.
20.
25.
30.
35.
40.
45.
55.
10.
20.
23.
35.
40.
45.
48.
50.
53.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
. .0.
0.
0.
0.
0.
0.
0.
0.1366667E-02
0.1366667E-02
0.0
0.0
0.8333332E-03
0.8333332E-03
0.1666665E-03
0.1666665E-03
0.1333334E-03
0.1333334E-03
0.0
0.0
0.8888885E-03
0.8888885E-03
0.5999999E-03
0.5999999E-03
0.5833332E-G3
0.5B33332E-03
0.6111111E-03
0.6111111E-03
0.5333330E-03
0.5333330E-03
0.3999998E-03
C.3999998E-03
0.3666666E-03
0.3666666E-03
0.3999991E-03
0.3999991E-03
0.3666654E-03
0.3666654E-03
0.3999996E-03
0.3999996E-03
0.3666687E-03
0.3666687E-03
0.1666641E-03
0.1666641E-03
0. H66677E-03
0.1 166677E-03
0.1222218E-03
0.1222218E-03
0. 1333332E-03
0.1333332E-03
0.1111137E-03
O.U11137E-03
0.0
0.0
0.2999997E-03
0.2999997E-U3
0.4666664E-03
0.4666664E-03
0.5555556E-03
0.5555556E-03
0.4999998E-03
0.4999998E-03
0.5555553E-03
0.5555553E-03
0.5833332E-03
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
3 .
J.
3.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
u.
0.
0.
0.
0.
0.
0.
0.
0.
3.
0.
0.
0.
0.
0.
0.
0.
1366667E-02
1366667E-02
0
0
8333332E-03
8333332E-03
1666665E-03
1666665E-03
1333334E-03
1333334E-03
0
0
8888885E-03
8838885E-03
5999999E-33
5999999 L— 03
5833332E-03
5833332E -03
6111111E-03
61111UE-03
5333330F-03
533333JE-33
3999998E-03
3999998E-03
3666666F-03
3666666F-03
3999991E-03
3999991E-03
3666654E-03
3666654E-03
3999996E-03
3999996E-03
3666687E-03
3666687E-03
1666641E-03
1666641F-03
1166677E-03
1166677E-03
1222218E-03
1222218E-03
1333332F-03
1333332E-03
1111137E-03
1111137E-03
0
0
2999997E-03
2999997E-03
4666664E-03
4666664E-03
5555556E-03
5555556E-03
4999998E-03
4999998E-03
5555553E-03
5555553E-03
5833332E-03
0.1366667E -02
0.1366667E-02
0.0
0.0
0.8333332E-03
0.8333332E-03
0.1666665E-03
0.1666665E-03
0.1333334E-03
0.1333334E-03
0.0
0.0
O.B888885E-03
0.8888885E-03
0.5999999E -03
3.5999999E-03
0. 5833332F-03
0.5B33332E-03
0.6111111E-03
0.611 1111E-03
0.5333330C -03
0.5333333E-03
0.3999998E-03
0.399Q998E-03
0. 3666666E-33
0.3666666E-03
0.3999991E-03
0.3999991E-03
0.3666654E- J3
U.3666654E-03
0.3999996E-03
0.3999996F-03
0.3666687E-03
0.3666687E-33
0. 1666641E-03
0.1666641E-03
0.1160677F-03
0.1166677E-03
0.1222218E-03
0.1222218E-03
0.1333332E-33
0.1323332E-03
0.1111137E-03
0. 1111137E-33
0.0
0.0
0.2999997E-03
0.2999997E-03
0.4666664E-03
0.4666664E-03
0.5555556E-03
0.5555556E-03
0.4999998E-03
0.4999998E-03
0.5555553E-03
0.5555553E-03
0.5833332E-03
O.i 366667F-02
0.1366667' 02
0.0
0.0
0.8333332E -03
0.8333332C-03
0.16666655-03
0.1666665E 03
0.1333334F -33
0.1333334=
0.0
0.0
-03
0.8888-385E-03
0.88883P5E
0.5999999F
J.5999999E
0.5853332^
0.5833332F
3.6111111E
0 . 6 1 1 i I \ ! F
0.5333330E
0. 5333330E
0.399999BE
0.3999998F
j. 3666666;
0. 3666666c
0.3999991E
0. 3 999 9 91 r
0.3666o5 •' J "
O.'ho'jf-tS -0- W
j.l?^334--..- ^
H
0.0 fD
u. 6k 8P dri5 -0 ' W
O
0. 5999<3'V3.. - J_J W
s
0.58332!,?- 0 ' 3
I 1
0 . 1 1 1 1 1 : 1 , - J T y
T3
3.5;333-U-; r £
rt
0 . -i ^ 9 S1 Q '-i 8 - 0 ''X*
O
rt
0. 3999991 t J^ "2
P
o.r-ofiifcs*- -o, rf
t~*
w
0. 3666687' J • f-|-
H-
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1
J. i;2221h: -j- _
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1-^
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M
0.0 q;
0)
0.2999°97C -O'i flj
0.46666642 -0 5
0. 555555s - -0~-
3. 499V99P-. - j1
0.5555553T Oj
0. 5R3:?3'- 0"
-------
FPA
Yrit
inv|TH
JL
HHIJP MI NUT c. SFCUNO hINO V
TEMPERATURE
SOLAP RADIATION ATMQS PPES P E I A T I V t HUKR'ITY
CO
O
OS
1973.
1973.
' 97? .
".97?.
1973.
197?.
' 973.
1973.
1973.
' 97'.
I 973.
1973 .
•.97-.
1973.
1 973.
• 97? .
1973.
• 97'.
1973.
1973.
1973.
1973.
1973.
1973.
197'.
1973.
1973 .
1973.
1973.
1973.
1973.
197?.
1973.
1973.
1.973.
1973.
1973.
1 9 73 .
1973.
1973.
197^.
1973.
1973.
1973.
1.973.
1973.
1973.
1973.
1973.
1973.
!.973.
1973.
1973.
1973.
1 .
1.
1.
1.
1 .
1.
1.
1 .
1.
1.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
7.
9.
9.
9.
9.
9.
9.
9.
1 ,
I .
1.
1.
I .
1.
1 .
1.
1 .
1.
". .
1.
1 .
' .
1.
1.
.
1 .
I.
1
3.
8.
R.
8 .
8.
3.
8.
a.
3.
8.
17.
17.
17.
17.
17.
17.
17.
17.
17.
17.
30.
30.
30.
30.
JO.
30.
?0.
30.
30.
30.
1.
1.
1.
1.
1.
1.
1.
0.
0.
0.
\J.
J.
3.
0.
0.
3.
0.
3 .
0.
0.
0 .
3.
0.
0.
0.
1.
0.
0.
3.
0.
0.
3.
0.
0 •
0.
0.
0.
u .
0.
0.
3.
0.
0.
0.
'0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
3.
0.
0.
0.
0.
3.
3.
3.
0.
0.
3.
J.
0.
3.
0.
0.
0.
J.
J.
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3.
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0.
3.
3.
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0.
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0.
0.
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0.
0.
0.
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0.
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0.
0.
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0.
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0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.1U34024E
0.1034024E
0.1034J24E
0. 1G34024E
0.1034024?
0.1034024E
0. 1034024E
0.1034024C
0.1034024E
J. 1034024E
0.9980130E
0.9980130E
0. 9980130E
0.9980133E
0.9980130E
0.998013 JE
0.9980130E
0.9980130E
0. 9980130E
0.9980130?
0.1008302E
0.1008302E
0.1008302E
0. 1008302E
0.1008302E
0.1008302E
0.1008302E
0.1008332E
0.1008302E
0.1008302E
0.8843361E
0.8848361E
0.8348361E
0.8848361?
0.8848361E
0.8848361E
0.8848361E
0.8848361E
0.8848361E
0.8848361E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.8076703E
0.2932305E
0.2932305E
0.2932305E
0.2932305E
0.2932305E
0.2932305E
0.2932305E
03
03
03
03
03
03
03
03
03
03
02
02
02
02
02
02
02
02
02
02
03
03
03
03
03
03
03
03
03
03
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
03
03
03
03
03
03
03
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
J
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.2852777E
.2852777E
.2852777E
.2852777E
.2852777?
.2852777E
.2852777E
.2852777E
.2852777E
.2852777E
.2666666E
.26&6666E
.2666666F
.2666666F
.2666666E
.2666666E
.2666666?
.2666666?
.2666666E
.2666666E
.2514444E
.2514444E
.2514444F
.2514444E
.2514444E
. 2514444E
.2514444E
.2514444E
.2514444E
.2514444E
.2645555E
.2645555E
.2645555E
.2645555E
.2645555F
.2645555E
.2645555E
.2645555E
.2645555E
.2645555E
.2747777E
.2747777E
.2747777E
.2747777E
.2747777E
.2747777E
.2747777E
.2747777F
.2747777E
.2747777E
.2411110E
.2411110E
.2411HOE
.2411110E
.2411110E
.2411110E
.2411110E
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
32
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
02
O.SOOOOOOE
0.5003000E
3.50JOUOOE
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.50JOOOOE
0.5000000E
J. 50JOOOOE
0.5000000E
0.50JOOOOE
0.5003303E
0.500000JE
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5000000E
0.5500000E
0.5500000E
0.5500000E
0.5500000E
0.5500000E
0.5500000E
0.5500000E
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
Oi
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
01
OL
01
01
0.10133001
0.101330JE
3. 10133 JOE
0.1013300E
0.1013300C
0.10133 J3E
0.1013300E
0.1013300E
0. 1 3133 JOE
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1015300E
3.10133006
0.1013300E
0.101330JF
0.1013300E
0.1013300E
0. 1013300E
0.1013300E
0.1013300F
0.101330JE
0.1013300?
0.1013300E
0.1013300E
0.1013300T
0.1013300?
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.10133JOE
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300?
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
0.1013300E
04
04
04
04
04
J4
04
04
04
04
04
J4
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
04
O.oOOOOOOE 00
O.'jQQQJQOF 00
J.6'.OOJO)f 00
0.6000000E CO
0.6000000E GO
3.63'3uJOOt 03
0.6000000? 00
0.6000000F 00
J.60300J3F 'JO
0.6000000? 00
0.6500000? 00
0.6500000? 00
0.6500000E 00
0.6500000E 00
0.65003JOE 00
0.6500COOF 00
0.6500000F 00
J.6500000E 00
0.6500000E 00
0.6500000E 00
0.8200000F 00
O.S200000E 00
0.3200000? 00
0.3200000? 00
0.3200000E 00
0.3200000? 00
0.8200000? 00
0.3200000E 00
0.8200000E 00
0.8200000E 00
0.3000000E 00
0.8000000E 00
0.3000000E 00
0.3000000E 00
0.8000000E 00
0.8000000E 00
0.3000000E 00
o.aooooooE oo
0.3000000E 00
0.30000JJE 00
0.8000000E 00
0.8000000E 00
0.8000000E 00
0.3000000E 00
0.3000000E 00
0.8000000E 00
0.8000000F 00
0.8000000E 00
o.aooooooE oo
0.8000000E 00
0.7500000E 00
0.7500000E 00
0. 7500000E 00
0.7500000E 00
0.7500000E 00
0.7500000E 00
0.7500000E 00
O
o
rt
H-
3
(D
-------
THETA
DHTAB ARRAY, SOIL TYPE 1
O(THETA) DIFFUSIVITY H(THETA) PRESSURE HEAD
SIGMA 0 DELTA THETA
00
O
1
2
T>
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
19
20
2?
22
23
0.600000E-01
0.800000E-01
0.100000E 00
0.120000E 00
0.140000E 00
0.160000E 00
0.180000E 00
0.200000E 00
0.220000E 00
0.240000E 00
0.260000C 00
0.280000E 00
0.300000E 00
0.320000F 00
0.340000E 00
0.36000QF 00
0.380GOOE 00
0.400000E 00
0.420000E 00
0.440000E 00
0.460000F 00
0.480COOE 00
0.500000F 00
0.100000E-06
0.999999E-06
0.600000E-05
0.100000E-04
0.300000E-.04
0.530000E-04
0.730000E-04
0.900000E-04
0.150000E-03
0.300000E-03
0.430000E-03
0.600000E-03
0.700000E-03
0.800000E-03
0.900000E-03
0.950000E-03
0.100000E-02
0.130000E-02
0.160000E-02
0.180000E-02
0.200000E-02
0.700000E-02
0.100000E-01
-0.600000E 06
-0.900000E 05
-0.400000E 05
-0.10000JE 05
-0.700000E 04
-0.470000E 04
-J.200000E 04
-0.100000E 04
-0.800000E 03
-0.680000E 03
-0.570000E 03
-0.450000E 03
-0.330000E 03
-0.220000E 03
-0.100000E 03
-0.900000E 02
-0.770000E 02
-0.600000E 02
-0.500000E 02
-0.400000E 02
-0.200000E 02
-0.100000E J2
0.0
0.200000E-08
0.220000E-07
0.1420005-06
0.342000E-06
0.942001E-06
0.200200E-05
0.346200E-05
0.526200E-05
0.826200E-05
,).142620E-04
0. 22 862 0£-04
0.348620E-04
3.488619E-04
0.648620E-04
0.828619E-04
0.101862E-03
0. liL862c -03
J. 147862E -03
3.179B62E-03
0.215862; -03
J.255862t-03
J.395861E-03
0.5958625-03
THFTA
OHTAB ARRAY, SOIL TYPE 2
O(THETA) OIFFUSIVITY H(THETA) PRESSURE HEAD
SIGV4 [) DELTA THETA
2
3
4
5
6
7
8
9
10
11
12
•13
14
15
16
17
18
19
20
21
22
23
0.500COOE-01
0.700000E-01
0.9GOOOGC-01
0.110COOE 00
0.1300006 00
0.1500JOE 00
0.170000F 00
0.190000E 00
0.21300JF 00
0.230000E 00
0.250000E 00
0.270COOE 00
0.290000E 00
0.310000F 00
0.330000E 00
0.350000E 00
0.370000F 00
0.390000E 00
0.410000E 00
0.430000E 00
0.450COOE 00
0.470000E 00
0.490000E 00
0.680000E-05
0.860000E-05
0.13JJOOE-04
0.230000E-04
0.400000E-04
0.680000E-04
0.120000E-03
0.180000E-03
0.280JOOE-03
0.400000E-03
0.560000E-03
0.300300E-03
0.120000E-02
0.170000E-02
0.240000E-02
0.320000E-02
0.440000E-02
0.600000E-02
0.800000E-02
0.110000E-01
0.150000E-01
0.190000E-01
0.260000E-01
-0.600000E J6
-0.900000E 05
-0.400000F 05
-0.100000E 05
-0.70000DE 34
-0.470000E 04
-0.200000E 04
-0.100000E 04
-3.800000E 03
-0.680000E 03
-0.570000E 03
-3.4500JOE J3
-0.330000E 03
-0.220000E 03
-0.100000E 0^
-0.900000E 02
-0.770000E 02
-0.600050E 02
-0.500000F 02
-0.400000E 02
-J.200000E 02
-0.100000E 02
0.0
J.1360005-06
0.309000E-06
J.5680JOE-06
0.10?800E-05
0. 182800E-05
J. 318800J-05
0.55R800E-05
•J.913300E-05
0. 147880C-04
0.227880E-04
0.339880E-04
0.4998bO'-04
0. 739879E-04
J. 107988r--03
0 . 1559flac-03
0.219988E-03
3.307988E '03
J.427988E-C3
0.587987:-03
0.807997E-03
3. 110799E-02
0.1487995-02
J.200799F-02
O
O
3
ft
H-
CD
-------
BEGIN PESTICIDE SIMULATION
CO
O
00
WATERSHED NAME: P-01
PESTICIDE NAME: DIPHFNAMID
START DATE:
JUL 8, 1973, 16 HRS, 52 MIN, 0.0
FNO 04TF:
JUL 16, .'973, 21 HRS, 30 MIN, 0.0
PLANT OATF.-
JUN 13, 1973, 0 HRS, 0 MIN, 0.0
MATURITY DATE:
SEP 12, 1973, 0 HPS, 0 MIN, 0.0
HARVEST OAT?:
NCW 1, 1973, 0 HPS, 0 MINI, 0.0
ZONE # SOIL TYPE
1
2
3
4
5
6
7
8
9
10
SFBL LM
SERL LM
LT CLAY
LT CLAY
LT CLAY
LT CLAY
LT CLAY
LT CLAY
LT CLAY
LT CLAY
AREA
CM**2
1699679.
38080912.
59488752.
26668768.
20072400.
42856160.
22055360.
16996784.
24685808.
17320528.
ZONE *
1
2
3
4
5
6
7
8
9
10
TO
21
1
10
3
6
7
1
1
5
8
100.000
100.000
33.333
12.500
66.667
66.667
100.000
75.000
20.000
50.000
SEC
SEC
SEC
SEC
SEC
SLOPE
PERCENT
4.000
3.000
4.000
3.000
3.000
2.000
4.000
4.000
2.000
•4.000
WATE°SHED ZONE DEFINITION
LENGTH WIDTH DEMSITY
CM CM GM/CM**2
2667.
26098.
12953.
8762.
9143.
11048,
9905.
6857.
3429.
6857.
000
496
996
996
996
996
996
996
000
996
2286.
3347,
5714,
4762.
3238.
5333,
5714,
3809,
6476,
3619.
000
085
996
496
500
996
996
999
996
499
1.600
1.600
1.600
1.600
1 .600
1.600
1.600
1.600
1.600
1.600
RUNOFF DESCRIPTION
TO
0
0
10
10
10
10
0
10
10
10
0.0
0.0
66.667
87.500
33.333
33.333
0.0
25.000
80.000
50.000
TO
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
JULIAN OATE
JULIAN OATE
JULIAM DATE
JUL I AM DATE
JULIAN OATE
SEDIMENT
INCREMENTS
3
3
3
3
•3
3
3
3
3
3
.ODO
.000
.OJO
.0 JU
.000
.000
.QJ:
.ooc
.000
.000
TO
0
0
0
0
0
0
0
0
0
0
2441 873.
2441881.
2441847.
2441938.
2441938.
NO.
LAYERS
15
' 5
15
15
! 5
15
15
15
15
15
. JOO
.000
.000
.OOu
.000
.000
.000
.000
.000
.000
0
0
'J
0
0
0
Q
0
0
u
20277 778'! 00
395b33330 00
soouoouor oo
500000000 00
500^0000^ 00
LAYS'-
CM
V
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
1.0 JG
-. . 000
1.000
1 . J j 0
i.OOO
1.000
1. 3JC
] .000
1.030
1. 3 JO
Sample SCRAM Input/Ov
rt
d
rt
^H
P-
CO
ft
•
iQ
\
O
0
13
rt
H-
p
d
(Tl
IU
-------
INITIAL CONDITION OUTPUT
JUL 8, 1973, 16 MRS, 52 MIN, 0.0
SEC
JUL MM DATE ? **1 873 . 2v2 77 7 78"
RAINFALL RATE =CM/SEC
0.8889E-03 0.8889E-03 0.8889E-03 0.8889E-03 0.88R9F-03 0.8889E-03 0.8889E-03 O.S883E-03
0.8«89F~0"i 0.68P-f J:
CO
O
PROFILE
THETA
1
2
3
5
6
7
8
9
10
1.1
12
13
15
CIT
1
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
2
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
3
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0. 070
0.070
0.070
0.0
ZONE
*
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
DEPTH PROFILE
ZONE *
5
0.3
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
6
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
7
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.0
9
0.0
0.500
0.061
0.062
0.063
0.06*
0.065
0.066
0 . 36 7
0. 068
0.069
u.073
0.070
0.070
0.070
0.0
9
0.0
0.500
O.U61
0.062
0.063
0.06*
0.065
0.066
0.067
0.068
0. 069
0.073
0.070
0.070
0.070
0.0
K
0.0
0.503
0 . j 6 1
0.3',^
O.Ubi
0 . J 6 *
0.065
0.066
J . J 6 7
0.06P
0.069
0 . J 7 0
0.070
O.J70
0.073
0.0
cn
P»
•0
(D
CD
O
s
2
*
i — |
M
£
rt
*\
0
fj
rt
i—*
\r>
H-
Cfl
rt
p-
in
O
O
d
CD
-------
NOPMAl CONDITION OUTPUT
J'JL 8, L973, 17 H»S, 3 MIN,
0. 0
SEC
JULIVN QATE 2441873. 208333331 00
RAINFALL MTF =CV/SEC
0.5832G-03 0.5833E-03 0.5B33E-03 0.5833E-03 0.5833F-03 0.5833E-03 0.5833E-03 0.5833E-03 0.5833F-03 0.5633E 03
ZONE DEPTH PROFILE
GO
I—"
O
PPPPUE
THFTA
2
3
4
b
6
7
e
q
n
1 1
12
13
'.4
15
r IT
OISSOLVEO
i
2
-3
4
5
6
7
8
9
10
11
12
13
ADSOR BED
1
2
3
5
6
7
8
9
10
11
12
13
]
0. 744
0.480
0. 440
0. ?69
0.061)
0.064
0.065
0. 066
0.067
i/. 068
0.069
0.070
j. D7 j
0.070
0.070
0. 568
Prcnr IDE
0.37?1? 02
0.289E 00
0.3b3c-03
0.2336-06
0.21 6r- 09
0 . 2 2 6F - 1. 2
0.239C-15
0.253E-18
0.270F-21
0.283'"-24
0.309F-27
0.335F-30
0.362p-33
PESTICIDE
0.989t 01
0.458F 00
0. 118F-02
0.150F-05
0.280E-08
0.5826-11
0.1226-13
0.257F-16
0.5426-19
0 . 1 1 5E - 2 1
0.244E-24
0.523F-27
0.112F-29
2
0.0
0.421
0.394
0. 144
0.063
0.064
u.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.337
0.373E 02
0.277? 00
0.322E-03
0.219E-06
3.2167-09
0.226E-12
0.239E-15
3. 2536-13
0.2706-21
0.288E-24
0.309E-27
0.335E-30
0.362E-33
0.967E 01
0.441E 00
0.1016-02
0.142E-05
0.280E-08
0.582E-11
0.122E-13
0.257E-16
0.5426-19
0.115E-21
0.244E-24
0.5236-27
0.112E-29
3
u. 0
0.488
0.328
0. 068
0.063
0.064
0. 065
0.066
0.067
0.068
0.069
0.070
0. 070
0.070
0.070
3.262
0.364E 02
0.298F DO
0.?78F-03
0.242E-06
0.230E-09
0.236E-12
0.247E-15
0.262E-18
0.278E-21
0.296E-24
0.318E-27
0.344E-30
0.372E-33
0.960E 01
0.298E 00
0.713E-03
0.111E-05
0.2196-08
0.452E-11
0.944E-14
0.199E-16
0.419F-19
0.885E-22
0. 1886-24
0.404F-27
0.863E-30
4
0.0
0.484
3.328
0.068
J.063
3.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.257
0.366E 02
0.298E 00
0.378E-03
0.242E-06
0.230E-09
0.2366-12
0.247E-15
3.262E-18
0.2786-21
0.296E-24
3.318E-27
0.344E-30
0.372E-33
0.963E 01
0.298E 00
0.7136-03
0. 111E-05
0.2196-08
0. 4526-11
0.944E-14
0.199E-16
0.419E-19
0.8856-22
0.1886-24
3.404E-27
0. 863E-30
ZONE *
5
0.0
0.490
0.328
0.068
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.264
0.363E 02
0.298E 00
0.378E-03
0.242E-06
0.230E-09
0.2366-12
0.247E-15
3.262E-18
0.278E-21
0.296E-24.
0.318E-27
0.344E-30
0.372E-33
0.9586 01
0.2986 00
0.713E-03
0.111E-05
0.2196-08
D.452F-11
0.944E-14
0.1996-16
0.4196-19
0.8856-22
0. 1886-24
0.4046-27
0.863E-30
6
0.025
0.495
0.329
0.068
0.063
0. 364
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.269
0.361E 02
0.298E 00
0.378E-03
0.242E-06
0.2306-09
0.236E-12
0.247E-15
0.2626-18
0.278E-21
0.296E-24
0.318E-27
0.344E-30
0.372E-33
0.955E 01
0.2986 00
0.713E-03
0.111E-05-'
0.219E-08
0.452E-11
0.944E-14
0.199E-16
0.419E-19
0.8856-22
0.1886-24
0.4046-27
0.8636-30
7
0.018
0.495
0. 329
0.068
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.269
0.361E 02
0.298F 00
0.378E-03
0.242F-06
0.230E-09
0.2366-12
0.247E-15
0.262F-18
0.278E-21
0.2966-24
0.318E-27
0.344E-30
0.372E-33
0.955E 01
0.298E 00
0. 7136-03
0.111E-05
0.219E-08
0.452E-11
0.944E-14
0.199E-16
0.419E-19
0.885E-22
0.188E-24
0.404E-27
0.8636-30
8
0.141
0.495
3.329
0.068
0.063
0.064
0.065
0.066
0 . 06 7
0.068
0.069
J.070
0.070
0.070
3.073
0. 269
0.361E 02
U.298E 00
0.378E-03
0.242F-06
0.230E-09
0.236E-12
0.247E-15
0.262E-18
0.278F-21
0.296E-24
0.31SF-27
0.344E-30
0.372E-33
0.955E 01
0.298F 00
0.713E-03
0.111E-05
0.219E-08
0.452E-11
0.944E-14
0.199E-16
0.419E-19
0.885E-22
0.188E-24
0.404E-27
0.863E-30
9
0.0
0.476
0.327
0.068
0.063
0. J64
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.248
0.371E 02
0.298E 00
0.378E-03
0.242E-06
0.230E-09
0.236E-12
0.247E-15
0.262E-18
0.278E-21
0.296E-24
0. 318E-27
0.344E-30
0. 372E -33
0.970E 01
0.298E 00
0.713E-03
0.1116-05
0.219E-08
0.452E-11
0.944F-14
0.19^6-16
0.419E-19
0. 885E-22
0.188E -24
0.4046-27
0.8636-30
10
0.268
0.495
0.329
0.068
0.063
0. 364
0.065
0.066
0.067
0.068
0.069
0. 370
0.070
0.070
0.070
0.269
0.361E 02
0.298F 00
0.378E 03
0.242E-36
0.230E-09
0.236F 12
0.247E-15
U.262E-18
0.2786 -21
0.296E-24
0.318E-27
0.344E -30
0.372E-33
0.955E 01
0.298E 00
0.7 13E-03
0.111E-05
0.219E -08
0.452E-11
0.944E -14
C. 1996-16
0.419F-19
0.8856 -22
0.1886-24
0.404E-27
0.8636-30
CO
V*J
3
i— i
CD
CD
O
[3Cl
3>
ts
H
P
p
rt
O
rt
*"0
d
rt
tr1
H-
01
H-
iQ
1
n
o
£3
rt
H-
B
(D
-------
ZONt
SEDIMFNT
LOAD
RUNOFF TOTAL
RATE PESTICIDE
CM/S MICROGRAMS
CO
1
1
3
4
c
6
7
R
9
10
DROP ILf
pcpTM
1
i
1
4
5
6
7
F
q
1 0
1 1
12
13
0. 46GPAMJ
0.1760E 02
0.1020E OJ
0.35595-04
0.1507F-07
0.1455r-10
0.1522E-12
0. 1
-------
NOCMAL CONDITION OUTPUT
JUL 8, 1973, 17 MRS, 10 MINI, 0.0
SEC
JULIAM DATE ?44-1873.21 5277781 Co
RAINFALL RATF =CM/SEC
0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000t-0i 0.40UOC-03 U.4000F OJ
ZONE DEPTH PROFILE
CO
I—>
to
PROFIL11
THETA
1
2
3
4
5'
6
7
8
9
10
il
12
13
14
15
CIT
DISSOLVED
1
2
3
4
5
6
7
8
9
10
11
12
13
ADSORBED
1
2
3
4
5
6
7
8
9
10
•11
12
13
1
1.234
0.457
0.452
0.412
0.291
0.069
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.931
PESTIC IDE
0.365E 02
0.491E 00
0. 219E-02
0.295E-05
0.541F-09
0.243E-12
0.245E-15
0.257E-18
0.2726-21
0.2896-24
0.310E-27
0.335E-30
0.362E-33
PESTICIDE
0.980F 01
0.485E 00
0.2606-02
0. 5136-05
0.363E-08
0.458E-11
0.9346-14
0.1956-16
0.410E-19
0.865F-22
0.184E-24
0.394E-27
0.8426-30
2
0.0
0.436
0.427
0.376
0. 123
0.064
0.065
0.066
0.067
0. 068
0.069
0.070
0.070
0.070
0.070
0.677
0.378E 02
.3.5326 00
0.128E-02
0.575E-06
0.243E-09
0.233E-12
0.242E-15
0.255E-18
0.271E-21
0.288E-24
0.309E-27
0.335E-30
0.3626-33
0.102E 02
0.5936 00
0.2486-02
0.234E-05
0.231E-08
0.450E-11
0.930E-14
0.1956-16
0. 4106-19
0.8656-22
0.1846-24
0. 3946-27
0.8426-30
3
0.033
0.482
0.455
0. 166
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.480
0.361E 02
0.588E 00
0.208E-02
0.7306-06
0.280E-09
0.2516-12
0.255E-15
0.266E-18
0.280E-21
0.297E-24
0. 3196-27
0.345E-30
0.373F-33
0.955E 01
0.444E 00
0.194E-02
0.212E-05
0.2456-08
0.4686-11
0.9616-14
0.2006-16
0.4216-19
0.887E-22
0.189E-24
0.4046-27
0.8636-30
4
0.027
0.482
3.455
0.165
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.479
0.361E 02
0.588F 00
0.208E-02
0.7296-06
0.2806-39
0.251S-12
0.255E-15
0.2666-18
0.280E-21
0.297E-24
0.3196-27
0.345E-30
0.3736-33
0.955E 01
0.4446 00
0. 194E-02
J.212E-05
0.2456-08
0.468E-11
0.961E-14
0.200E-16
0.421E-19
0.8876-22
0.189E-24
0.4046-27
0.8636-30
ZON6 *
5
0.038
0.482
0.455
0.166
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.480
0.3616 02
0.588E 00
0.208E-02
0.7306-06
0.280E-09
0. 2516-12
0.255E-15
0.266E-18
0.280E-21
0.297E-24
3.319E-27
0.345E-30
0.3736-33
0.955F 01
0.4446 00
0.1946-02
0.2126-05
0. 2456-08
0.468E-11
0.9616-14
0.2006-16
0.421E-19
O.B87E-22
0.1896-24
0.404E-27
0.8636-30
6
0.077
0.482
0.455
0.166
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.480
0.361E 02
0.587E 00
0.20Bc-32
0.731E-06
0.280fc-09
0.251E-12
0.255E-15
0.266E-13
0.280E-21
0.297E-24
0.319E-27
0.345E-30
0.373E-33
0.9556 01
0.4446 00
0.1946-32
0.2126-05
0.245E-08
0.4686-11
0.961E-14
0.200E--16
0.4216-19
0.887E-22
0.189E-24
0.404E-27
0.863E-30
7
0.068
0.432
3.455
0.166
0.063
0.064
0.065
0.066
0.067
0.068
0.069
0. J7J
J. 07 J
0.070
0.070
0.480
0.361F 02
0.587E 00
0.208F -02
0.731E-06
0.280E-09
0.25 IE- 12
0. 2556-15
3.266E-18
0.280E-21
0.297E-24
3 .319E-27
0.3456-30
0. 3736-33
0.955E 01
0.444E 00
0.194E-02
0.212E-05
0.2456-08
0.4686-11
0.9616-14
0.2006-16
0.4216-19
0.8876-22
0.189E-24
0.4046-27
0.8636-30
8
0.21 8
3.432
3.455
0. 166
0.063
3. 364
0.065
J.066
? . 36 7
0. 068
0.069
0.073
0.070
0.070
J.. 'jl J
j.48u
0.361E 02
0.587^ 00
0.208E-G2
3.731i-06
U.?8jL:-o9
0.25U-12
0.25bfc-15
0.265E-18
0.280E-21
0.297E -24
0.319E-27
0.?45E-30
0.373E-33
0.9555 01
0.444E 00
u. 1 94E-02
0.212E-05
0. 2456-08
J. 4686-11
0.961E-14
0.200E-16
0.42 IE- 19
0.337E-22
0.189E-24
0.434E-27
0.8636-30
q
0.011
0.482
9.455
0 . ?. b 5
0.063
0.'364
0.065
0.066
3.067
0.068
0.069
0.070
0.070
0.070
0.379
0.47°
0.361E 32
0. 5S8E 00
0.2076-02
0.727E-06
0.280E-G9
0.251E-12
0.2556-15
0.266E-1 8
0.280E-21
9.297E-24
0. 319E-27
0.3456-30
0.373E -33
0.956E Oi
0.444E 00
0.1946-02
0.212E-05
0.245E-08
0.468E-11
0.961b -14
0.200E -16
0.42 IE- 19
0.887E -22
0. 189E-24
0.404E-27
0.863E-30
19
0.43:
0.482
0 . •+ b b
0. ',66
0.363
0. 364
0.065
0.366
0. J67
0.06d
0.069
j . o 7 :•
0.070
0.070
3.073
0.460
0.361= 32
0.587E OJ
0.208F 02
0.731F -06
0.280F -09
0 . 2 5 1 F - i i
U.255E-15
0.2666-13
0.?80F -21
C.297E-24
0.319F-27
C.345F-30
0.373E-33
0.955E O'l
0.444E CO
0.194E-02
0.212F-05
0.245F-08
0.46BE-11
0.961E -14
0 . 2 0 OF 16
0.42 IE- 19
0.887E-22
0.189F -24
9.404E-27
0. 863E-30
&>
g
•5
H-
0
CO
o
!5
S
s
M
.-
ri-
O
(H
rt
TJ
£
n-
i — i
r1
P.
en
rt
H-
3
LQ
1
O
O
rt
H-
C
fD
pi
-------
CO
l-l
CO
ZONE *
1
2
3
4
5
6
7
8
9
10
PROFILE
DEPTH
1
2
3
4
5
6
7
8
9
10
11
12
13
SEDIMENT
LOAD
GM/CM/SEC
0.1702E 00
0.0
0.6441E-02
0.4663E-02
0.5332E-02
0.6346E-02
0.8226E-02
0.2620E-01
0.21?3?-02
0.6644E-01
AVERAGE
PESTICIDE
DISSOLVED
MICKOGRAMS
0.1724E 02
0.2590E 00
0.4134E-03
0. 1297E-06
0.1961E-10
0. 1616E-13
0. 1667E-16
0.1766E-19
0.1892E-2?
0.2038E-25
0.2218E-28
0.2399E-31
0.2594E-34
RUNOFF
RATE
CM/S
0.1951E-01
0.0
0.2049E-03
0. 1981E-03
0.2422E-03
0.2345E-03
0.4696E-03
0.1930E-02
0.2072E-03
0.3712F-02
AVERAGE
PESTICIDE
ADSORBED
MICROGPAMS
0.1544E 02
0.7407E 00
0.3298E-02
0.3910E-05
0.4089E-08
0.7445E-11
0.1523E-13
0.3187E-16
0.6697E-19
0.1412E-21
0.3004E-24
0.6435E-27
0. 13746-29
TOTAL
PESTICIDE
MICROGRAMS
0.3334E 02
0.3405E 02
0.3368E 02
0.3368F 02
0.3368E 02
0.3368? 02
0.3368E 02
0.3368E 0?
0.3368E 02
0.3368E 02
TOTAL
PESTICIDE
MICROGRAMS
0.3266E 02
0.9997E 00
0.3711E-02
0.4040E-05
0.4108E-08
0.7461E-11
0.1529E-13
0.3189E-16
0.6699E-1V
0.1413E-21
0.2004E-24
0.6435E-27
0. 1374E-29
ACCUMULATED RUNOFF:
WATER - 24046.LITERS
SETIMFNT = 279.KILOGRAMS
ACCUMULATED PESTICIDE LOSS:
IN WATER = 7.25GF A^S/HFCTAPE
ON SEDIMENT = 0.03GRAv\S/ri =
INSTANTANEOUS PiSTICK'E I ? S <•
B16.8')''''ICt;OG(.4MS/L I T'-f
o ,
TOTAL WATEP LOSS
FROM EVAPOTRANSPIRATI3N
0. LITERS
ACCUMULATED INFILTRATION
WATFR LOSS = 0. LITERS
WATEP RALANCF:
WATFR IN = 0.5519909E 06
WATEP OUT = 0.5519946F 06
ZONE 1 INFILTRATION
ZONE 2 INFILTRATION
ZONE 3 INFILTRATION
ZONE 4 INFILTRATION
ZONE 5 INFIl TRATION
ZONE 6 INFILTRATION
ZONE 7 INFILTRATION
ZONE 8 INFILTRATION
ZONE 9 INFILTRATION
ZONE 10 INFILTRATION
r OF PESTICIDE APPLIED
IN WATFP = 0.2153
ON SEDIMENT = O.Q008
01-
1C i
LITERS
LITEP S
RATE =
RATE =
RATE =
RATE^
RATE =
PATE =
RATE =
RATE =
RATF =
RATE =
0.5569E-03
0.5333E-03
0.3165E-03
0.3168E-03
0.3164E-03
0.3162E-03
0.3162E-J3
0.3162E-03
0.3177E-03
0.3162E-03
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
n
o
3
rt
H-
(D
-------
NPCVAL riNQITnN OUT°UT
JDL fit !973, 17 HP 5, 20 MIN,
0. 0
SEC
N DATE 2441873.222222220
I- MMFtLL PATF =CM/SFC
0.4000C-03 C.4000E-03 0.4000C-03 0.4000E-03 0.4001E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-G3 0.4000E 02
ZONE .3EPTH PROFILE
P R n F T L r
THCTA
1
2
•a
4
5
6
7
«
9
10
1 1
12
13
1 4
15
r IT
U' SSOL VF
1
?
T
A
5
6
7
8
9
10
11
12
13
4nsc"= R=D
i
2
3
4
5
6
7
8
9
10
11
12
13
1
0. 738
0.472
0.463
0. 436
C . ^ 9 4
0.235
0.067
O.C66
0. 067
''.• .068
0.069
o.o ro
0.070
0.070
0.070
1.253
; BfcSTICIPE
0.352F 02
J.7'j3c 00
0.573F-02
0.286E -04
0.371F-07
3.i34F- n
0.265F-15
0.261E-18
0.274E-21
0.290E-24
0.310E-27
G.335F-30
0.363E -33
PEST 1C ICE,
0.959E 01
0.598E 00
0.458F-02
O.ia5fc-04
0.436F-07
0.173E-10
0.978E-14
0.! 97E-16
0.412E-19
0.867E-22
0.184E-24
0.394E-27
O.B42E-30
2
O.J
0.440
0.425
0.392
0.318
0.083
•j • 06 5
0.066
0.067
0. 068
0. 069
0.070
0.070
0.070
0.070
0. 907
0.371E 02
0.727E 00
0.411F-02
0.927E-05
3.232E-08
0.284F-12
0.250F-15
0.259E-18
0.273E-21
0.289E-24
J.310E-27
0.335F-^0
0.362E-33
0.101E 02
0.713E 00
0.494F-02
0.120C-04
0.872P-08
0.505F-11
0.947E-14
0.196E-16
0.411E-19
0.867E-22
0.184F-24
0.394E-27
0.842E-30
3
0. 01 =-02
0.999F-05
0.931E-09
0.281F-12
0.263E-15
0.270E-18
0.?82E-21
0.29SE-24
0.319E-27
0.345E-30
0.373E-33
0.940F 01
0.566E 00
0.438,^-02
0.988E-05
0.497E-08
0.500E-11
0.978E-14
0.202E-16
0.423E-19
0.889E-22
0.189F-24
0.404E-27
0.863E-30
4
0. Jll
0.491
D. 467
0.307
J.074
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.653
0.351F 02
0.889E 00
0.825E-02
0.998E-05
D.930E-09
0.281E-12
0.263E-15
0.270E-18
0.282E-21
3.298E-24
J.319E-27
0.345E-30
0.373E-33
0.940E 01
0.566E 00
J.438E-02
0.987E-05
0.497E-08
0.50JE-11
0.973E-14
0.202E-16
0.423E-19
0.889E-22
0. 189E-24
0.404E-27
0.863E-30
ZONE #
5
0.016
0.491
0.467
0.307
0.074
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.653
0.351E 02
0.-1R8E 00
0.825E-02
0. 100E-04
0.932F-09
0.281E-12
0.263F-15
D.270E-18
0.282E-21
0.298E-24
D.319E-27
0.345E-30
0.373E-33
0.940fc 0!
0.566E 00
0.438i=-02
0.988F-05
0.497E-08
0.500F-11
0.978E-14
0.202E-16
0.423E-19
0.889E-22
0.189E-24
0.4064
0.065
0. 366
0. 367
0 . 0 6 f •
0.069
0.070
0.070
0.070
0.070
0.653
G.351F 02
0.838E 00
0.825F-02
0.100t-04
0.933E-09
0.291E-12
0.263F-15
0.270F-18
0.282E-21
0.298E-24
0.319E-27
0.345E-30
0.373E-33
0.940E 01
C.566E 00
0.438E-02
0.989E -05
0.498E-08 .
0.500E-11
0.978E-14
0.202E -16
0.423E-19
0.889E-22
0.189E-24
0.404-E-27
0.863E-30
CD
fu
3
13
M
fD
CO
O
TS
2
*
|— |
3
V
Cl
n-
\
o
£
rt
13
£
rt
tn
H-
en
rt
H-
3
^
I
Q
o
3
rt
P-
3
C
fD
fr
-------
C71
ZONE *
1
2
3
4
5
6
7
8
9
10
PROFILE
DEPTH
1
2
3
4
5
6
7
9
9
10
11
12
13
SEDIMENT
LOAD
GM/CM/SEC
0.7509E-01
0.0
0.2846E-02
0.2111E-02
0.2392E-02
0.2932E-02
0.3482E-02
0.1478E-01
0.98!9F-03
0.2829F-01
AVERAGE
PESTICIDE
01 SSOLVED
MICROGRAMS
0.1709E 02
0.3955E 00
0.2440E-02
0.2011E-05
0.9401E-09
0.3218E-13
0.1728E-16
0.1793E-19
0.1906E-22
0. 2 04-6 E- 2 5
0.2222E-28
0.2401E-31
0.2595E-34
RUNOFF
RATE
CM/S
0. 1851E-01
0.0
0.7809E-04
0.67615-04
0.9371E-04
0. 1425E-03
0.2870E-03
0.1638E-02
0.4623E-04
0.2839E-02
AVERAGE
PESTICIDE
ADSORBED
MICROGRAMS
0.1519E 02
0.9345E 00
0.7124E-02
0.1770E-04
0.1474E-07
0.9980E-11
0.1560E-12
0.3215E-16
0.6727E-19
0. 1416E-21
0.3007E-24
0.6439E-27
0.1375E-29
TOTAL
PESTICIDE
MICROGRAMS
0.3327E 02
0.3398E 02
0.3361E 02
0.3361E 02
0.3361E 02
0.3361E 02
0.3361E 02
0.3361E 02
0.3361E 02
0.3361E 02
TOTAL
PESTICIDE
MICROGRAMS
0.3?28E 02
0.1330F 01
0.9564E-02
0. 1971E-04
0.1568E-07
0.1001E-10
0.1562E-13
0.3217E-16
0.6729E-19
0.1416E-21
0.3008E-24
0.6439E-27
0.1375E-29
ACCUMULATED RUNOFF:
WATEP = 44430.LITERS
SEDIMENT = 492.KILOGRAMS
ACCUMULATED PESTICIDE LOSS:
IN WATER = 13.40GRAHS/HECT6RE
ON SEDIMENT = 0.05GRAMS/H^cTO?E
INSTANTANEOUS
TOTAL WATER LOSS
FROM fVAPHTRANSPIRAT ION
0. LITERS
ACCUMULATED INFILTRATION
HATER LOSS = 0. LITERS
% OF PESTICIDE APPLIED
IN HATER = 0.3982
ON SEDIMENT = 0.0015
FATE OF PESTlCint LHSS
HATER
WATER
WATER
ZONF
ZONE
ZONE
ZONE
ZONE
ZONE
ZONE
ZONF
ZONE
ZONE
BALANCE:
IN = 0.6140701E 06
HUT =
1
2
3
4
5
6
7
8
9
10
= 0.6140770E 06
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
LITERS
LITERS
RATE =
R ATE =
PATE =
RATE =
RATE =
RATE =
RATE =
RATE =
RATE =
RATE =
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
6849E-03
3667E-03
3760E-03
3762E-03
3760E-03
3760E-03
3760E-03
3760E-03
3763E-03
3760E-03
CM/SEC
CM/SEC
CM/SEC
CM/ SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
o
o
3
(-1-
H-
(D
-------
NORMAL CONDITION OUTPUT
JUL 8, 1973, 17 MRS, 30 MIN,
0.0
SEC
JULIAN DATE 244 1 873. 22 9 1666 70 30
CO
RAINFALL RATE =CM/SEC
0.4000F-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000E-03 0.4000F-Oi 0.4000F-0;
ZONE DEPTH PROFILE
PPOFILF
THETA
1
2
3
4
5-
6
7
8
9
10
11
12
13
14
15
CIT
DISSOLVED
1
2
3
4
5
6
7
8
9
10
11
12
13
ADSORBED
1
2
3
4
5
6
7
8
9
10
11
12
13
1
0.803
0.471
0.467
0.446
0.417
0.371
0. 160
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.516
PESTICIDE
0.348E 02
0.924E 00
0.116E-01
0.103E-03
0.629E-06
0. 100E-08
0.527E-13
0.396E-18
0.276E-21
0.291E-24
0.311E-27
0.336E-30
0.363E-33
PESTICIDE
0.953E 01
0.703E 00
0.692E-02
0.416E-04
0.231E-06
0.612E-09
0.220E-12
0.251E-16
0.414E-19
0.869E-22
0.184E-24
0.395E-27
0.842E-30
2
0.0
0.447
0.439
0.414
0.371
0.214
0.067
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.137
0.362E 02
0.931E 00
0.861E-02
0.531E-04
0.113E-06
0.123E-10
0.337E-15
0.263E-18
0.275E-21
0.290E-24
0.3HE-27
0.336E-30
0.363E-33
U.100E 02
0.825E 00
0.763E-02
0.336E-04
0.856E-07
0.463E-10
0.113E-13
0.198E-16
0.413E-19
0.868E-22
0.184E-24
0.395E-27
0.842E-30
3
0.024
0.490
0.473
0.389
0.130
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.795
0.347E 02
0.118E 01
0.202E-01
0.106E-03
0.375E-07
0.121F-11
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
0.742E-02
0.397E-04
0.437E-07
0. 1186-10
0.101E-13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
4
0.019
0.490
0.473
0.389
0.130
0.064
0.065
0.066
0.067
O.ObS
0.069
0.070
0.070
0.070
0.070
0.795
0.347E 02
D.118E 01
0.202E-01
0. 106E-03
J.374F-07
0.121E-11
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
3.742E-02
0.397E-04
0.436E-07
0.118E-10
0.101E-13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
ZONE H
5
0.027
0.490
0.473
0.389
0.130
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.795
0.347E 02
0.1 18E 01
0.202E-01
0.106E-03
0.375E-07
0.121E-11
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
0.742E-02
0.397E-04
0.437E-07
0.118E-10
0.101E-13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
6
0.061
0.490
0.473
0.389
0.130
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.795
0.347E 02
0.118E 01
0.202E-01
0.106E-03
0.376E-07
0.122E-11
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
0.742E-J2
0.397F-04
0.437E-07
0.118E-10
0.101E-13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
7
0.054
0.490
0.473
0.389
0.130
0.064
0.065
0.066
0.067
0.068
0.069
0. J70
0.070
0.070
0.070
0.795
0.347E 02
0.113E 01
0.202E-01
0. 106E-03
0.376E-07
0.122E-H
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
0.742E-02
0.397E-04
0.437E-07
O.U3E-10
0.101E-13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
8
0.191
0.490
3.473
0.389
J. 130
3.064
0.065
0.066
J. 067
0.068
0.069
3.07J
0.070
0.070
3.070
0. 795
0.347E 02
0.1 18E 01
0.202F-01
0. 106fc-03
0.376E-07
0.122E-11
0.279E-15
G.274F-18
0.284E-21
0.299E-24
0.320E-27
0.345E-30
0.373E-33
0.933E 01
0.667E 00
0.742E-02
0.397E-04
0.437E-07
J. 118E-10
0.1 OIF- 13
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0.864E-30
9
0.009
0.490
0.473
0.388
0.129
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.794
0.347E 02
0.118E Jl
0.202E-01
0. 106E-03
0. 772E-07
0.120E-11
0.279E-15
0.274E-18
0.284E-21
0.299E-24
0. 320E-27
0.345E-30
0.373E-33
0.933F 01
0.668E 00
0. 742E-02
0.397E-04
0.434E-07
0. 118E-10
0.101E-13
0.204E-16
0.425E-19
0. 891E-22
0. 189E -24
0.405E-27
0.864E-30
10
0.353
0.490
0.473
0.389
0.130
O.D64
0.065
0.066
0. J67
0.06H
0.069
0.070
0. 070
0.070
0. 370
0.79b
0.347F J2
0. 1 1SE 01
0.202F-01
C. 106E-03
C.376F-G7
0.122E-11
0.279F-15
0.274E-18
0.284F-21
0.299E-24
0.320E-27
0.345F -30
0.373F-33
0.933F 01
0.667E 00
0.742E-02
0.397E-04
0.437E-07
0.118E-1J
0.101E -1?
0.204E-16
0.425E-19
0.891E-22
0.189E-24
0.405E-27
0. 864E-30
CO
PJ
3
V
I—1
(T)
CO
o
^
jig
H
3
T3
e
rt
O
ft
*V
C
ft
tr1
H-
w
rt
H-
3
|O
^
I
n
o
P
rt
H-
3
C
(D
PI
MJ
-------
00
I—1
-q
TN~ *
1
2
3
4
5
I,
7
8
9
10
nF II c
DFPTM
^
2
3
4
5
6
7
f
9
10
1 1
12
13
SFOIMENT
LOAD
r,y/cu/SEc
C.1062E 00
0.0
0.2985E--02
0.2173E-02
J.2482F-32
C.2993E-02
0.3860E-32
0. 1793F-31
0.9980t-0?
0.3424F-01
AVERAGE
PESTIC IDE
DISSOLVED
0.1685E 02
0.5297E 00
0.7164E-02
0.17'!E-04
0.2767E -07
3. 1612E-10
3.3667F-15
0. 1909E-19
0. 1921E-22
0.2054F-25
0. ^226E-28
0.2404E-31
0. 2597t-34
RUNOFF
RATE
cn/s
0. 1900E-31
0.0
0.1368E-33
0. 1287E-03
0.1622E-03
0.1812F-03
0.3635E-03
0.1718E-32
0. 1240F-03
0.3146E-32
AVERAGE
PESTICIDE
ADSORBED
o!i507EA02
0.1099E 01
0. 1183?- Jl
0.6288E-04
0.1065E-J6
0. 1234E-J9
0.4990E-1'
0.3330E-16
0. 6757E-19
0. 1419E-21
0.3011F-24
0.6443E-27
0.1375E-29
TOTAL
PESTICIDE
«ICPnGRA«IS
0.3322E 02
0.3393E 02
0.3356C 02
0.3356E 02
0.3356E 0?
0.3356E 02
0.3356E 02
3.3356E 02
0.3356E 02
0.3356E 02
TOTAL
PESTICIDE
0.3192E 02
0.1628E 01
3.1899E-31
0. 8019E-04
0.1342E-36
3. 1365F-)9
0.5026E-13
0.3331E-16
0.6759E-19
0. 1419E-21
0. 3011E-24
0.6443E-27
0. 1375E-29
SFr.'IMfNT =
RUNOFF:
61704. L ITEt- S
636. KILOGRAMS
ACCUMULATED PESTICIDE LOSS:
IN WATER = 18.60GRAyS/HFCT4RE
ON SEDIMENT = 0.06GRAMS/HECTARE
INSTANTANEOUS PESTIfllE I CSS
B10.91VICROGPl'-'S/LITi=c
aTbR LTSS
EVAPCU9 ANSP IRAT I JN
0. I ITFPS
ATCUN
wnT=R
W/NTcP
W A T F o
WATF".
ZONE
ZON1-
zr'Nr
7PNF
ZHMF
Z1NF
70NF
ZONE
ZONF
ZnNE
ULATfn
LOSS
TALAMC
IN =
OUT =
1
2
3
4
5
6
7
8
9
10
=
P
0
0
0. LIT;PS
0.6761488E 06
0.676!594F 06
INFILTRATION
IN-FILTPST ION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTOATION
INFILTRATION
INFILTRATION
INFILTRATION
INFILTRATION
LITERS
L ITEF S
R ATE =
R ATF =
f ATF =
RATE=
RATF =
RATE =
PATE =
BATE =
"ATE =
RAT6 =
0.4563E-03
0. 3667E-03
0.2443E-03
0.2444E-03
0.2443E-03
0.2442E-03
0.2442E-03
0.2'.^2F-03
O^^'t'fE-OS
0.2442E-03
CM/ScC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SFC
C-VSEC
C1/SEC
CM/SEC
CM/SEC
OF PESTICIDE APPLIED
IN WATEP = 0.5526
)N SEOIMFNT = 0.0019
PATF OF PESTICIDE LOSS
64021.34MICROGPAMS/LITF=/H*
21.65MICPOGRAMS/GR:,"/HR
o
o
3
rt
H-
3
d
0)
-------
NORMAL CONDITION OUTPUT
JUL 8, 1973, 17 MRS, 40 MIN, a. 0
SEC
JULMN DATE 2441873.236111110 OC
RAINFALL RATE =CM/SEC
0.1667E-03 0.1667F-03 0.1667E-03 0.1667E-03 0.1667E-03 0.1667E-03 0.1667E-03 0.1667E-03 0.1667E-03 0.1667E 02
ZONE DE°TH PROFILE
CO
h-'
00
PROFILE
THETA
i
2
3
4
5
6
7
Ft
9
10
11
12
13
• 4
15
HIT
DISSOLVE
1
2
3
4
5
6
7
8
9
10
11
12
13
ADSORBED
1
2
3
4
5
6
7
8
9
10
11
12
13
1
0.902
0.488
0.471
0.452
0.429
0.394
0.328
0.094
0.067
0.368
0.069
0.070
0.070
0.070
0.070
1.774
0 PFSTICinE
0.336E 02
0.115E 01
0. 201--01
0-267E-03
0.284F-05
0.229E-07
0.614E-10
0.838E-14
0.323E-19
0.305E-24
0.311E-27
0.336E-30
0.363E-33
PESTICIDE
0.933E 01
0.800E 00
0.955E-02
0.727E-04
0.560E-06
0.386E-08
0.140E-10
0.880E-14
0.681E-18
0.894E-22
0.1 85E-24
0.395E-27
0.843E-30
2
0.0
0.457
0.449
0.426
0.395
0.340
0.115
0.066
0. 067
0.068
0.069
0.070
0.373
0.070
0.070
1.367
0.352E 02
0.114E 01
0.153F-01
0.157E-03
0.121E-05
0.252E-08
0.205F-12
3.893E-1 8
0.278E-21
0.291E-24
0.311E-27
0.336E-30
0.363E-33
0.983E 01
0.929E 00
0.107E-01
0.636E-04
0.346E-06
0.106E-08
0.490E-12
0.406E-16
0.416E-19
0.870E-22
0.185E-24
0.395E-27
0.842E-30
3
0.023
0.499
0.477
0.420
0.220
0.066
0.065
0.066
0.067
0. 068
0.069
0.070
o.oro
0.070
0.070
0.932
0.338E 02
0.146E 01
0.370E-01
0.495E-03
0. 127E-05
0.242E-09
0.327E-14
0.287--18
0.2 86F.-21
0.301E-24
0.320F-27
0.346E-30
0.373E-33
0.918E 01
0.756E 00
0.106F-01
0.983E-04
0.347E-06
0 .266E-Q9
0.430E-13
0.209E-16
0.426E-19
0.893E-22
0.189E-24
0.405E-27
0.864E-30
4
0.017
0.499
3.477
0.420
0.220
0.066
0.065
0.066
0.067
j.068
0.069
0.070
0.070
0.070
0.070
0.932
3.338E 02
3.146r 01
0.370E-01
0.495E-03
0. 127E-35
3.241E-09
0.326E-14
0.236E-18
0.286E-21
0.301E-24
0.320E-27
0.346F-30
0.373E-33
0.918E 01
3.757E 00
0. 106E-01
0.983E-04
0.347E-06
3.265F-09
0.430E-13
0.209E-16
0.426E-19
0.893E-22
0. 189E-24
0.405E-27
0.864E-30
ZONE *
5
0.027
0.499
0.477
0.420
0.220
0.066
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
0.932
0.338E 02
3.146E 01
0.370F-01
0.495E-33
3.127E-35
0.242E-09
3.328E-14
0.287E-18
0.286E-21
0.301E-24
0.320E-27
0.346E-30
0.373E-33
0.918E 01
0.756E 00
0.106E-01
0.983E-04
0.347F-06
3.266E-09
3.431E-13
0.209E-16
0.426E-19
0.893E-22
0.189E-24
0.405E-27
0.864E-30
6
0.066
0.499
0.477
0.420
0.220
0.066
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.073
0.070
0.932
0.338E 32
O.I46E 01
0.370E-3X
0.495E-33
0.127E-05
0.243E-39
0.329E-14
3.287E-19
0.286E-21
0.301F -24
0.320E-27
0.346E-30
0.373E-33
0.918E 31
0.756E 00
0.106E-01
C.983E-04
0.347E-06
0.266E-09
0.432E-13
0.209E-16
0.426E-19
0.893E-22
0.189E-24
0.405E-27
0.864E-30
7
0.058
0.499
0.477
0.420
0.220
0.066
0.065
0.066
3.067
0. 068
0.069
3.370
0.070
0.070
3.070
0.932
3.339E 02
0.146F 01
0.370E-01
0.495E-03
3.127E-05
0.243E -09
0.329E-14
0.287F-18
0.286E-21
0.30 IE -24
3.323E-27
0.346E-."',0
0.373E-33
0.918E 01
0.756E 00
3. 106E-31
0.983E-04
0.347F.-06
0.266E-09
0.432E-13
0.209E -16
0.426F-19
0.893E-22
0. 189E -24
0.405E-27
0.864E-30
8
0.217
3.499
3.47 1
0.420
3.220
J . Oo6
0.065
0.066
J.367
j . 0 6 8
0.069
J. 373
0.070
0.070
J . 0 7 3
0.932
0.339- 02
3.146F 01
0.373F-01
0.495E-33
U.127E-05
0.243F -09
0.329E-14
0.287C-18
0.286E -21
0.301F-24
0.320E-27
0.346F-30
0.3735-33
0.918H 01
0.756E 00
J.106E-31
0.983--04
0.347F-06
0.266E-09
0.432E-1 3
0.209E-16
0.426E-19
0. 893fc -22
0.189E-24
0.405E-27
0.864E-30
9
0.004
0.499
3.477
0.420
0.219
J.366
0.065
0.066
0.067
0.068
0.069
0 . 3 70
0.0 70
0.070
0.073
0.931
0.338E J2
J.146F 01
0.i70E-01
0.4956-03
u. 126E-05
0.239E-09
0.323E-14
0.286E-18
0.286E-21
0.301E-24
0. 320E-27
0.346E-3C
0.373E -33
0.918E Oi
0.757E 00
0.106E-31
0.983E-04
0.346E -06
0.264E-09
0.427E-13
0.209F-16
0.426E-19
0.893E-22
0.1B9E -24
0.405E-27
0. 864F-30
10
O.'»09
J.4O9
:.477
0.423
0.220
0. Joe
0.065
3.366
3. J6?
0. 368
0.069
3 . .1 7 :•
C.070
0.370
3.373
0. 93?
3.338F 3?
3 . 1 46 F 01
0.3 70F -31
0.495E- J3
C.127F -05
0.243E-09
C.329E-14
0.?87F -18
0.296F -21
0.301F-24
C. 320!- -77
0.346F-30
0. 373E -33
0.918F 01
0.756F 00
Li. 1061-01
C.9B3C -04
0.347F-06
0.266F-39
0.432E-1J
0.209E-16
C.426E-19
0.893E -2;
0. 189E -2^-
0.40'iE-?7
0.864E-30
CO
fu
3
10
I— i
(D
cn
O
s
c
^
H
'O
C
rt
"X^
O
£j
ri-
rt
L_,
H-
cn
rt
H-
3
^Q
*
O
O
rt
H-
3
e
CD
DJ
-------
CO
CWE « SFOI'^'.T
LOAD
j " / C w / - F C
1 C . 2 7 9 1 F - 0 1
' 0 . .j
' C.2746F-3?
4 0.2091F -02
S 0.220H-J2
6 0.24>:5F-02
7 C.2^80f -02
f 3.6339F.-C2
9 0 . 9 7 9 ' F - 0 ':
li; L. 136'-)F -01
AVtR/»r,c
[OF HE PSST ir in;
DF^TH o I S50LVFC
'1 1 c aoGfl AN s
I. 0. : 674F 02
1 0.6f2hF 00
'- C. 1?9<7-F- 31
4 0 . ': 0 4 7 F - o "'
5 0 . 2 2 J ? r • 0 f i
»• 0. 7'"J?3F- jri
7 0.5773F-12
:J 0.561 7^-16
c 0.2374F-21
10 0.207 IF -25
11 u.2?iQF-?b
12 G.2405F-31
1 ? 0 . ? 5 9 R F - 3 ',
K'l^OFF
RiT ;
0.1 /S
0.2044--..1
o . o
0.1062P-03
0. 9036F-Q4
0. 1331--0?
0. 1 81 1 F- 03
0.3554E -03
3.19 J4F- 32
0. 4001 F- 04
0.3600E -02
AVERSE
PESTIC I JF
A n s ' i P P ~ T;
v icbn'jO c. MS
0.1482C Oc
C.1245E 01
3. 167fac-31
0. 14 76F-03
3.5893E -06
3. 11 2yE -ufi
0.2370F-'_1
0.1 44 1^ .;4
j. 1 733F-1P
0. 1426fc-2l
0.3014^-24
0. 644Ce -27
0.1375C-29
TOTAL
°FSTir [D1;
•MCPrKiPA-,3
3.3316" 32
0.3337E 02
C.3350F 32
0.3353" 32
0.3350" 32
0.3353E 02
0.3350? 02
3.3350E 02
3.3350E 02
3.3350C 32
TOTAL
PEST1CI1F
MICPO^PAMS
0.3156? 0?
0.1939E 01
0.3077E-31
0.252;?- 03
0.3J94E-36
0. 1921C-3H
0.2940?-!!
0. 149 7E -14
u. 1735F-18
0. 1426E-21
0.3014"--24
J.6446E-2 7
j. 1375E-29
' Cf.UMNL 4T|. o PHMr.tr
W4Trp =
Sf.OI'iF'IT =
5. LI TFP S
ACCUMULATEP PFSTICIOL LOSS:
ON SEDIMENT = 0.0b0c
INSTANTtNt f 'JS PFSTICI
B11.72wif f
0. 27
'E L1SS
TT T 'I W1TFP Lrc-S
mr:M f Vir-X'TP ANC P IP 4T IrJ:-i
0. LITEPS
ACCU"IJLA TFD INflLTPATION
WATrPLOSS= 0. LITERS
ZON
jk Tfo HAL ANCF :
WATf-P IN = 0.7"-',82771F 06
OUT = 0.7382417F Oft
1 INFILTP4TIQN
p JNF IL JP AT ION
3 I',T IL TOATIQN
4 INFILTRATION
5 INFILTPATIC1N
6 INF I LTOATI UN
7 IMFILTPATION
P INFILTBATinN
q [NFILTPATION
10 INFILTRATION
70K'F
ZHNF
ZCMF
L I TEFS
LITERS
PATE =
PATE =
P ATE =
P ATT =
0.4u44E
0.3667E
0.2134E
0.2135?
0.2134E
0.2134F
0.21346
0.21346
0.21356
0.2134E
32 C"/SfcC
03 CM/SiC
02 CM/SfC
02 CM/SEC
02 CM/SEC
02 CM/SEC
02 CM/SEC
J2 CM/SEC
-02 CM/SEC
-02 CM/SEC
t OF PESTICIDE APPLIED
IN WATE» = 0.7279
(IN SEni«£NT = 0.0023
rc OF PE^T ICiri- i C'S:
72B95..7tMICPnr,i.AI-'S/LITEt /HP
O
o
3
rt
H-
CD
-------
NORMAL CONDITION OUTPUT
JUL 8, 1973, 17 HRS, 50 MIN, 30.56577 SEC
JULIAN DATE 2441873.24340933D
CO
to
o
RAINFALL RATE =CM/SEC
0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-03 0.1167E-0:
ZONE DEPTH PROFILE
PROFILE
THETA
i
2
3
4
5
6
7
8
9
10
11
12
13
14
15
CIT
DISSOLVED
1
2
3
4
5
6
7
8
9
10
11
12
13
ADSORBED P
1
2
3
4
5
6
7
8
9
10
11
12
13
1
0. 166
0.476
0.473
0.458
0.438
0.413
0.372
0.238
0.071
0.068
0.069
0.070
0.070
0.070
0.070
1.992
PESTICIDE
0.337E 02
0.140E 01
0.321E-01.
0.583E-03
0.870E-05
0. 121E-06
0.142E-08
0.745E-11
0.661E-14
0.909E-18
0.406E-23
0.726E-30.
0.363E-33
ESTICIOE
0.935E 01
0.894E 00
0.126E-01
0.115E-03
0.108E-05
0.103E-07
0.888E-10
0.478E-12
0.907E-15
0.574E-18
0.486E-22
0.621E-27
0.843E-30
2
0.0
0.432
0.425
0.412
0.392
0.356
0.247
0.072
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.456
0.361E 02
0.136E 01
0.246E-01
0.365E-03
0.490E-05
0.473E-07
0.141E-09
0.333E-13
0.370E-18
0.465E-24
0.312E-27
0.336E-30
0.363E-33
0.997E 01
0.103E 01
0.141E-01
0.104E-03
0.788E-06
0.596E-08
0.228E-10
0.198E-13
0.286E-17
0.115E-21
0.185E-24
0.395E-27
0.843E-30
3
0.0
0.4B3
0.472
0.431
0.306
0.083
0.065
0.066
0.067
0.063
0.069
0.070
0.070
0.070
0.070
1.025
0.341E 02
0 . 1 76 E 01
0.585E-01
0.137E-02
0.147E-04
0.241E-07
0.745E-11
0.149E-15
0.455E-21
0.302E-24
0.321E-27
0.346E-30
0.373E-33
0.923E 01
0.841E 00
0.138E-01
0.179E-03
0.146E-05
0.399E-08
0.406E-11
0.828E-15
0.559E-19
0.896E-22
0.190E-24
0.405E-27
0.864E-30
4
0.0
0.482
O.t72
J.431
0.306
0.083
0.065
0.066
0.067
0. 068
0.069
0.070
0.070
0.070
0.070
1.023
0.341E 02
D.176E 01
0.585E-01
0. 137E -02
0. 146E-04
0. 241E-07
0.743E-11
0. 143E-15
0.454E-21
0.302E-24
0.321E-27
0.546E-30
0.373E-33
0.924E 01
0.841E 00
0.138E-01
0.179E-03
0.146E-05
0.399E-08
0.406E-11
0.826E-15
0.559E-19
0.896E-22
0.190E-24
0.405E-27
0.864E-30
ZONE #
5
0.0
0.483
0.472
0.432
0.306
0.083
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.026
0.341E 02
D.176E 01
0.585E-01
0. 137E-02
Q.147E-04
0.242E-07
0.747E-11
0. 149E-15
0.455E-21
0.302E-24
0.321E-27
0.346E-30
O.J73E-33
0.923E 01
0.841E 00
0.138E-01
0.179E-03
0.146E-05
0.399E-08
0.407E-11
0.830E-15
0.560E-19
0.896E-22
0.190E-24
0.405E-27
0.864E-30
6
0.0
0.489
0.478
0.437
0.307
0.083
0.065
0.066
0.067
0.063
0.069
0.070
0.070
0.070
0.070
1.044
0.338E 02
0.176E >Jl
0.585F-01
0.137E-02
0.147E-04
0.242E-07
0.749E-11
0.150E-15
0.456F.-21
0.302E-24
0.321E-27
0.346E-30
0.373E-33
0.918E 01
0.841E 00
0.138E-01
0.179E-03
0.147E-05
0.400E-08
0.408E-11
0.833E-15
0.561E-19
0.896E-22
0.190E-24
0.405E-27
0.864E-30
7
0.0
0.489
0.478
0.437
0.307
0. j83
0.065
0.066
0.067
0 . 06 8
0.069
0 . 0 70
0.070
0.070
0.070
1.044
0.338E 02
0.176E 01
0.585E-01
0.137E-02
0.147E-04
0.242E-07
C..749E-11
0.150E-15
0.456E-21
0.302E-24
0.32 IE- 2 7
0.346E-30
0.373E-33
0.919E 01
0.341E 00
0.138E-01
0.179E-03
0.147E-05
0.400E-08
0.408E-11
0.833E-15
0.561E-19
0.896E-22
0.190E-24
0.405E-27
0.864E-30
8
0.051
0.492
0.479
0.437
0.307
' 0. J83
0. 065
0.066
j. J67
0.06H
0.069
0.070
0.070
0.07J
J . 0 7 3
1.049
0.336E 02
0.1 76E 01
0.585E-01
0.137E-02
0. 147E-04
0.242E-0 1
0. 749E-11
0.150E-15
0. 456E-21
0.302E-24
0.321E-27
0.346F-30
0.373E-33
0.916E 01
0.841E 00
0. 133E-01
0.179E-03
0. 147E-05
0.400E-08
0.408E-11
0.833E-15
0.561E-19
0.896E-22
0. 190E-24
U.405E-27
0.864E-30
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
9
0.0
0.481
0.471
0.42H
0. 305
0.083
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
J. 0 7u
1.018
342E 02
176E 01
585E -01
137E-Q2
146E-04
240E-07
736E-11
146E-15
451F-21
302E-24
321E-27
346E-30
373E-33
924E 0'
842E 00
138E-01
179E-03
146E-05
397E-08
404E-U
819E -15
557E-19
B96E-22
190E -24
405E-27
864E-30
10
0.070
0.492
0.47S
0.437
0.307
0.083
0.065
0.066
0.067
o.ose
0.069
0 . j 7 0
0.070
0.070
0 . J 7 j
1.049
0.^36F J?
0 . 1 7t>E ul
0.5R5E 01
0. lj7E-0?
0.1^7E -04
0.242F. -07
0.749E -11
0.150r-15
0.456E -21
0.302E-24
0.321E-27
0.346E -30
0.373F-33
0 . 9 : fc E 01
0.841E 00
0.138E-01
0.1 79E-03
0.147F -05
0.4JOE-08
0.408E-11
0.833E--15
0.561E-19
0.b96F-2Z
0. 190F- 24
0.4}5E-27
0.864E-30
W
ff
3
TJ
M
0)
CO
O
S
C
^
1— 1
3
V
d
rt
\
o
d
rt
T3
d
rt-
\ '
|H
H-
cn
rt
H-
3
«T)
1
o
O
3
rt
H-
3
d
n>
ex
-------
CO
(S3
70NJF »
I
?
3
<.
s
f,
7
p
9
10
PRilF ILE
DCPTlM
:>
:i
4
5
f,
7
8
Q
1 0
1 '.
12
1 •'•
SFD I'-'FNT
LOAD
-jM/CM/SFC
0.9VJ9F-02
0* J
0.0
0. 3
0.0
0.0
0.0
O.U65F-02
0.0
C.3040E--02
AVfcMGE
PcSTtC 106
DISSOLVED
M ic ROGRAMS
0.1636F 02
0.7926r 00
0.227-9?-;!
0. 37476-03
0.15u9F-05
3.6
-------
SPECIAL CONDITION OUTPUT
JUL 8, 1973, '7 HPS, 59 MIN, 3.96273 Sf=C
JULIAN DATF 2441373.249^51420 JO
co
to
to
RAINFALL F a T F = r M
0.1222--03 u.1222
PROFILE
THETA
1
•>
^
4
5
6
7
R
9
10
11
12
1?
1 4
15
CIT
TISSDLVFQ
1
2
3
it
5
6
7
8
9
10
11
12
13
40SO° RED
1
?
3
4
5
k
7
8
9
10
11
12
13
1
0.0
0.472
0.469
0.45S
0.443
0.421
0. 590
0. -35
0. 115
J.068
0.069
3.070
0.070
0.070
2.154
PESTIC irc
0.336C 02
0.16JE 01
0.442E-01
0.987r~o3
0.182F-04
0. 305E-06
0.523C-08
3.825E-13
0.422E - 12
3.103F-14
0.128E-17
0.430E-21
0.925c-26
PESTIf IDE
0.933F 01
0.967E 00
0.152E-01
0.157E-03
0.167E-05
0.177E-07
0. 191E-09
0. 197F-11
0.105E-13
0.359E-16
0.833E-19
0.898E-22
0.192E-25
/SEC
F-03. 0.122
7
0.0
0.425
0.416
0.404
0.336
0.357
0.305
0.105
0. 067
0.068
0.069
0.373
3. 373
0.070
0.070
1.517
0.362E 02
C.155E 01
0.340E-01
0.635E-03
•3.108E-04
0.1 70E-06
3.189E--03
0.494F-U
0.339F-14
0.292E-18
0.603E-24
0.392E-30
0.363E-33
0.998E 01
0.111E 01
0.170E-01
0.144E-03
0.125F-05
0.126E-07
0.105F-09
0.376E-12
0.612E-15
0.294F-18
0.158E-22
0.432E-27
0.843E-30
2F-03 3.1222E-03 O.t?2:c-03 0.1222E-03 0.1222C-03
ZONE D:PTH PROFILE
3
0.0
0.483
0.469
0.431
0.338
0.118
0.065
0.066
0. 067
0. 068
0.069
0.070
0.070
0.070
0.070
1. 386
0.339F 02
0.203E 01
0.786F-01
0.244E-02
0.502E-34
0.276E-06
0.527E-09
0.30RE-12
0.192E-16
0.?82E~22
0.327E-27
0.346E-30
0.374E-33
0.921E ~01
0.906F 00
0.164E-Q1
0.250E-03
0.302E-05
0.167E-07
0.498E-1G
0.738F-13
0.294E-16
0. 129F-20
0.192E-24
0.405E-27
0.864E-30
4
0.0
0.480
3.469
0.430
0 . 3 ? 8
0. 118
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.084
3.339E 02
3.200E 01
0.786E-01
0.244E-02
3.501E-04
3.275F-06
3.525E-09
0.306E-12
0.190E-16
0.279E-22
3.327E-27
0.346E-30
0.374E-33
0.921E 01
0.906E UO
0. 164E-01
0.250E-03
0.302E-05
0. 167E-07
0.497E-10
0.736E-13
0.292E-16
0.128E-20
0. 192E-24
0.405E-27
0.864E-30
ZHN E *
5
0.0
0.480
0.469
0.431
0.339
0.113
0.065
0.066
0.367
0.068
0.069
0.070
0.070
0.070
0.070
1.087
0.339E 02
0.230E 01
0.785E-01
3.244E-02
3.502E-04
0.276E-06
0.527E-09
0.338E-12
0.193E-16
0.284E-22
3.327E-27
0.346E-30
0.374E-33
0.920F 01
0.906E 00
3.164E-01
0.250E-03
0.302E-05
0.168E-07
0.498E-10
0.739E-13
0.294E-16
0.129E-20
0.192E-24
0.405E-27
0.864E-30
6
0.0
0.482
0.472
0.439
0.343
3.119
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1. 105
0.338E 02
0.200E 01
0. 787F-01
0.245E-02
0.506E-04
0.2 f9E-06
0.533E-09
0.312E-12
0.196E-16
0.291E-22
0.327F-27
0.346E-30
0.374E-33
0.919E 01
0.905E 00
0. 164E-01
0.251E-03
0.304E-05
0.169E-37
O.SOIE-'.O
0.744E-13
0.297E-16
0.131E-20
0.192E-24
0.405E-27
0.864E-30
7
0.0
0.482
3.472
0.438
0.343
0.119
0.065
0.066
0. 367
0.068
0.069
0.070
0.070
0.070
0.070
1.105
3.338E 02
0.200E 01
0.787E-01
0.245F-02
0. 5 06 E- 04
0. 279F-06
0.533E-09
0.312E-12
0.196E-16
0.291F- 22
0.327E-27
0.346E-30
C.374E-33
0.919E 01
0.905E 00
3.164E-01
0.251E-03
0.304E-05
0.169E-07
0.50 IE- 10
0.744E-13
0.297E-16
0.131E-20
U.192F-24
0.405E-27
0.864E-30
0.1222t-03
8
0.0
0.491
J.480
0.45!
3.348
3.123
0. 065
0.066
3.367
0.068
0.069
0. 070
0. 070
0.070
3.073
1.141
0.334E 02
0.200E 01
0.78SE-01
0.245E-02
0.508^-04
0.289E-06
0.534E-09
0.312F-12
0.196E-16
0.2916-22
0.327E-27
0.346E-30
0.374E-33
0.912E 01
0.905E 00
0. 164E-01
0.251E-03
0.304E-05
0.169E-07
0.502E-10
0.745E-13
0.298E-16
0.131E-20
0.192E-24
0.405E-27
0.864E-30
0. 1222E-03 0.1222E -02
9
0.0
0.479
0.468
0.428
0.336
0.117
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.079
0.339F 02
0.200E 01
0.786E-01
0.243E-0?
0. 500E-04
0.273E-06
0.520E-C9
0.301E-12
0.186F-16
0.268E-22
0.327E-27
0.346F-30
0.374E-33
0.921E 01
0.906E 00
0.164E-01
0.250E-03
0.301E-05
0. 166E-07
0.494E-10
0.730E-13
0.288E-16
0.125E-20
0.192E-24
0.405E-27
0. 864E-30
10
o.c
0.492
0.481
0.45i
0.348
0. 120
0.065
0.066
0.067
0.068
0.069
0.070
0.070
0.070
0.070
1.142
0.333E 02
U.200E 01
0.788E -01
0.245E-02
0.508F-04
0.280E -06
0.534E-09
0.312E-12
0.196F-16
0.291E-22
0.;>27E-27
0.346O30
0.374F-33
0.911E 01
0.905E 00
0.164E-01
0.251E-03
0.304E -05
0.169E-07
0.502E-10
0.745E-13
0.298E-16
0.131E-20
0.192E-24
0.405E-27
0.864E-30
CO
PJ
3
^Tj
H-1
(D
CO
o
s
^
H
3
*T3
c
rt
c
rt
j^
rt
t^1
H-
C/l
rt
H-
iD
1
O
o
3
rt
H-
3
(D
\v
-------
a
y>
o
o
00
to
CO
ZONE *
i
2
3
4
5
6
7
8
9
10
PROFILE
•DEPTH
^
2
3
4
5
6
7
8
9
lu
11
12
13
SEDIMENT
LOAD
GM/CM/SEC
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
AVERAGE
P F S T I C I D E
DISSOLVED
MICPOGR 4*15
0.1613F 02
0.9120F 00
0.3241E-01
0. 8083E-03
0.7275F-05
0.4002E-07
0.2901E-09
0.149BE-11
0.471BF-14
0. 1405F-1&
0. 254QE-19
0. 1861F-22
0. 1981E-26
RUNOFF
RATE
CM/S
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
AVERAGE
PESTir ID;
40SOR9ED
M1CPOGP4MS
0.1484C 02
0.1509E 01
0. 26B7F-01
0.387?F-0^
0.476SF-05
0.3136E-07
0.14-1 5E-09
0.6163E-12
0. 24976-14
0.8741E-17
0.2471E-19
0.4194=:-2?
0.2292E-2?)
TOTAL
PESTICIDE
MICP.OGRAMS
0.3311E 02
0.3382E 02
0.3345E 02
0.3345E 02
0.3345E 02
0.3345E 02
0.3345E 02
0.3345E 02
0.3345F 02
0.3345E 02
TOTAL
PESTICIDE
MICPOGRAHS
0.3097F 02
3.2421E 01
3.5929E-01
0.1 196E-02
0. 1204E-04
0.7 138F-07
0.4316E-09
0.2114E-U
3.7215E-14
0.2279E-16
0.5020E-H
D.6055E-22
0.2490E-25
ACCUMULATED RUNOFF :
WATFR =
SEDIMFNT =
O.LITFcis
O.KILQGPAMS
ACCUMULATED PESTICIDt LOSS:
IN WATER - o.O GBAMS/HECTARF
ON SEDIMENT = 0.0 3RA^S/HECTARF
! NST ANTANF JUS PFSTlCMt
0. J i'ICHOGPA'..s/l
0.3 "KD ooo.vs/i
TOTAL WATEP LOSS
FRO-< EVAPOTRANSP IPAT ION
0. LITERS
ACCUMULATED INFILTRATION
WATER LOSS = 0. LITERS
WATEP BALANCE:
WATER IN = 0.781530QF 06
WATFP OUT = 0.7815529E 06
ZONE 1 INFILTRATION
ZONE 2 INFILTRATION
ZONE 3 INFILTRATION
ZONE 4 INFILTRATION
ZONE 5 INFILTRATION
ZONE 6 INFILTRATION
ZONE 7 INFILTRATION
ZONE 8 INFILTRATION
ZONE 9 INFILTRATION
ZONE 10 INFILTRATION
LITERS
LITEt< S
RATE =
RATE =
RATE =
RATE»=
RATE =
RATE =
RATE =
PATE =
RATE =
RATE =
0.1222E-03
0.1222E-03
0.1222E-03
0. 1222E-03
0. 1222E-03
0.1222E-03
0.1222E-03
0. 1222E-03
0.1222E-03
0.1222E-03
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
CM/SEC
C*/SEC
t, OF PESTICIDE APPLIED
IN WATFP = 0.0
ON SEDIMENT = 0.0
FATr OF PEST1CK-
0.0 WICC-J
0.0
T.? /MR
n
o
3
rt
H-
3
c
fD
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
REPORT NO.
EPA-600/3-76-066
3. RECIPIENT'S ACCESSION-NO.
4. TITLE ANDSUBTITLE
Simulation of Pesticide Movement on Small Agricultural
Watershed
5. REPORT DATE
September 1976 (Issuing Date)
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Ronald T. Adams and Frances M. Kurisu
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
3SL Incorporated
495 Java Drive
Sunnyvale, California 94086
10. PROGRAM ELEMENTNO.,
1BB039; ROAP/Task 21 AYP 11
1BA025; ROAP/Task 22 AEG 4
11. CONTRACT/GRANT NO.
68-01-2977
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Athens, Georgia 30601
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT
5TRACT
Simulation of Contaminant Reactions and Movement (SCRAM) is a computer simulation
designed to predict the movement of pesticides from agricultural lands. SCRAM is com-
posed of deterministic submodels which describe the following physical processes: in-
filtration, percolation, evaporation, runoff, sediment loss, pesticide adsorption and
desorption in the soil profile, pesticide microbial degradation in the soil profile,
and pesticide volatilization.
SCRAM predictions of these physical processes are compared to experimental data
furnished by the Southeast Environmental Research Laboratory in cooperation with the
Southern Piedmont Conservation Research Center. Simulated runoff for two small water-
sheds (less than 3 hectares) near Athens, Georgia, agrees reasonably well with experi-
mental data. Sediment loss is not as accurately predicted. Predictions of pesticide
loss in the runoff and on the sediment are in reasonable agreement with experimental
data if allowance is made for the effects of inaccurately predicting sediment loss.
Simulated pesticide movement in the soil profile differs from experimental measure
ments at the surface and below 10 cm. Simulated degradation rates are below measured
rates early in the season but are in closer agreement by the end of the season.
Volatilization losses for a single pesticide agree qualitatively with measured values.
The evapotranspiration model was not evaluated directly.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group
Pesticides
Mathematical models
Simulation
Surface water runoff
Hydrology
Watersheds
Pesticide transport
Sediment transport
Surface water quality
Pesticide degradation
12A
8H
6F
18. DISTRIBUTION STATEMENT
Release to public
19. SECURITY CLASS (ThisReport)
UNCLASSIFIED
21. NO. OF PAGES
342
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
324
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